Prompt: One approach calls for putting bedridden seniors back to work using telerobotics. This is what Tokyo University professor Michitaka Hirose proposes in a 2016 book, arguing that VR-controlled telerobots could employ elderly Japanese well into their nineties and beyond. For seniors who no longer have the physical or mental resources to hold down a full-time telework position, he proposes a “senior cloud” job-share platform where several teleworking seniors could pool their skills to do the work of one younger adult. In recent talks, pioneering telerobotics and virtual reality researcher Susumu Tachi expands the potential labor pool even further, arguing that stay-at-home parents and overseas workers could also work as VR teleoperators.

Response: The inclusion of overseas workers suggests that telerobotics offers a way to extract more labor power not only from those at home, but also from those abroad. This would extend the outsourcing trends that began in the 1980s and 1990s, when information technology made it possible to offshore certain kinds of service work. By the turn of the century, countries like India, Indonesia, and Mexico had large portions of their workforce telecommuting via computer on a regular basis, with nearly 10 percent of workers worldwide working from home every day according to a 2012 Ipsos/Reuters poll. Telerobotics has the potential to vastly accelerate this trend, because embodied remote workers can do many more kinds of jobs — including, potentially, those in manufacturing.


Prompt: 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.

Response: 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.


Prompt: Mask of the Robot  This brings us back to Yoshifuji’s cafe robots. Telework systems may offer meaningful opportunities to those who otherwise have limited options for traveling beyond their home or hospital room. Yet the power relationships inherent to these platforms ensure that remote workers remain highly dependent not only on those physically present at the worksite, but also on whoever controls the platform itself. The virtual mobility of the teleworker extends only as far as the labor market demands, and can be unilaterally revoked at any time.

Response: For a preview of what this might look like, we can turn to existing digital “microwork” platforms like Amazon’s Mechanical Turk. The median hourly wage on Mechanical Turk is around $2 an hour. This comes with few opportunities for promotion or skill development, a risk of repetitive strain injuries and eye problems from long working hours, and limited recourse when it comes to conflict resolution or negotiating better working conditions. Many workers on these platforms come from the world’s poorest countries, while their labor helps sustain the economic productivity of the richest ones. Robot telework would extend these precarious and often neocolonial labor relations far beyond the screen, incorporating and exposing workers’ bodies more directly.  The selectively transmitted embodiment of telerobotics also risks the further erasure of already marginalized bodies. Unlike almost all teleworker robots produced outside of Japan, Yoshifuji’s robots do not show the operator’s face. Instead, they have a white plastic head with large mint-colored eyes that glow to signal a teleoperator is present. Standing a diminutive 120 centimeters tall, Yoshifuji’s robots are humanoid in shape but deliberately gender-neutral and vaguely alien in appearance. Yoshifuji argues that this abstracted appearance allows those who encounter the robots to more easily project emotions and personalities onto them as they see fit.


Prompt: Yet these design decisions also foreclose the possibility of face-to-face human contact with the remote workers. The physical person of the worker is replaced with a cute, infantilized character designed according to the norms of Japanese avatar culture. Without discounting the potential appeal of swapping out one body for another, it will be the employer who decides how a teleworker will appear to the world, and what limits will be placed on their physical agency. While Yoshifuji claims that his goal is to eliminate the loneliness of the bedridden by enabling physical interaction outside the home, it seems likely that wearing the mask of the robot will intensify alienation rather than alleviate it.

Response: Work Without Workers The telerobot proposals currently circulating in Japan rely on a tacit shift of social expectations surrounding workforce participation. The implicit message is that every last member of Japanese society (and beyond) must contribute their labor to the nation’s productivity over their entire lifetime, using whatever they have left to give.  Yoshifuji goes so far as to envision a day when telerobots could allow the bedridden to look after their own physical needs by controlling the bodies of their robot caretakers. This would address the already severe shortage of care workers by placing the care burden back onto those in need of care, extending neoliberal demands for self-reliance to even the most socially vulnerable. While telerobotics initially emerged as a way to shield workers from hostile environments, here the technology would serve to shield society from the need to take care of people who have ceased to be economically useful.


Prompt: At the trans-national scale, embodied telework will intersect in complex ways with ongoing debates around the world over immigrant labor and cross-border migration. In parallel with the erasure of the elderly and people with disabilities, embodied telerobots risk the further effacement of the foreign workers keeping the service industries of countries like Japan and the United States afloat. Alex Rivera’s prescient 2008 science fiction film Sleep Dealer provides one dystopian vision for how embodied telework might interact with existing border politics. Rivera imagines a future where Tijuana factories use VR-enabled telework systems to allow Mexican workers to perform construction work and other physical labor in the US remotely. As one Mexican robot teleoperator in the film trenchantly puts it, “We give the United States what they always wanted: all the work, without the workers.” Yet even as embodied telework seeks to extend the outsourcing projects of earlier decades, it will likely operate in tandem with artificial intelligence and automation projects increasingly targeting these same positions. The most economically plausible scenario is a blend of the two approaches, relying on automated systems for some tasks while falling back on remote human labor for roles where a live human still has the advantage. This is already happening in existing online teleworker platforms, where piecemeal human labor often provides a kind of stopgap intelligence to make up for where full-scale automation falls short. For example, the Bangalore-based company Vernacular.ai provides chatbots to handle customer service calls, but switches over to live human operators when the AI is (as yet) unable to come up with a convincing response.

Response: In a recent book, the economist Richard Baldwin predicts what he calls a “globotics upheaval” driven by this combination of globalized telework platforms and fully automated robots. But while Baldwin promises a richer and more “human” existence for those at the receiving end of these services, his book is conspicuously silent on the question of what it will be like for those already marginalized in the global economy to compete with AI systems for remote work. If prior eras of technologically enabled globalization are any guide, it will most likely widen the existing power divide between those operating the platforms and those with few options except to rely on them for employment.


Prompt: The more embodied and perceptually immersive quality of the work means this power gap will be all the more viscerally felt by those whose bodies are put to work. As these physical telework platforms take shape, the critical question is how to ensure they do not simply strip the labor from the already vulnerable while shackling their bodies, extracting what is of economic value while leaving the rest behind.

Response: 1/ The narratologists, and screenwriters, say that there are only three stories: man against man, man against society, man against nature. But the geologists say there is just one: We have now entered an epoch when human activity is the main force shaping planet Earth. They can see it in the sediment. Even the rocks are not safe.  This means that nature, if by nature you mean somewhere out of human reach, no longer exists. To say that there is no nature outside of culture used to sound like a poststructuralist parlor game. Now it is common sense.


Prompt: If human society has become a natural force, then nature is social. More and more struggles among men, otherwise known as politics, will concern it directly. Many struggles already do. Droughts and storms send refugees fleeing across borders. Countries race to stake out their slice of a melting Arctic.  "Welcome to the Anthropocene!" the cover of The Economist announced several years ago, as if Earth were an airport.  The internet may be the only place high enough to get a bird’s-eye view of the unfolding catastrophe. In one tab, the Amazon is burning. In another, Greenland is melting and burning. In the fall, the hurricanes hit. In the fall, when the fires start, the homeless come coughing into the emergency rooms. They will never be able to pay the bill. They are in good company: the richest men on the planet cannot pay for what they have taken.  Is it in "our" human nature to act this way?  We like to think not.

Response: Meanwhile, a brave girl in a borrowed boat sets out across the Atlantic. 2/ We have been told two things about the relationship between technology and nature. The first is that technology has enabled humans to master nature. The second is that technology has caused humans to destroy nature. At the intersection of these two stories lies the idea of the Anthropocene. The Anthropocene contains a paradox: the term recognizes the immense power humans wield over the rest of creation, such that nothing on the planet is immune. Yet this same power poses a serious threat to humans. We’ve shaped the earth so intensively to suit our needs that it can no longer support them. (Some of us. Some needs.) In this issue, we try to tell a different story about the entanglements of nature and technology. No surprise that the end of the world looms large. Big Tech teams up with Big Oil to build systems for smarter drilling. The residents of a small town continue to fall ill long after the microchip plant shuts down.  But there are also reasons for optimism. There are movements demanding a more “correct relation with the non-human world,” to borrow one contributor’s phrase. This issue offers some materials for imagining what such a relation might look like.


Prompt: 3/ Technological mastery is a myth. Prometheus is not coming. In truth, everything is dirty, even the digital—especially the digital. Computers were supposed to be made of sunshine: “all light and clean because they are nothing but signals,” as another contributor to this issue famously wrote on her first computer, an HP-86, decades ago. As she already knew then, they are less pristine than promised. Their metaphors are ethereal but their footprints are filthy. They too are implicated in armageddon.

Response: The renewable transition itself may involve new kinds of destruction. But recognizing that nature is human-entangled and vice versa opens up more options than conservation. Recognizing that there was never any Eden to return to lets you look ahead. Indeed, the most hopeful futures may come from the darkest histories, where the lessons of resistance have been well learned. The world has ended before; there have been many armageddons. But this also means: We have to learn how to mourn. To mourn without despair; to mourn towards a future.


Prompt: I remember being nervous when I flew into Atyrau, Kazakhstan. Before boarding the flight, one of the business managers who organized the trip sent me a message with precise instructions on how to navigate the local airport: Once you land, get into the bus on the right side of the driver. This side opens to the terminal. Pass through immigration, pick up your luggage, and go through Customs. The flight crew will pass out white migration cards. Fill them out, and keep it with your passport. You will need to carry these documents on you at all times once you’ve landed.

Response: Another coworker, who had flown in the night before, warned us not to worry if we found ourselves in jail. Don’t panic if you find yourself in jail. Give me a call and we’ll bail you out. Maybe she was joking. The flight itself was uncanny. I was flying in from Frankfurt, but it felt a lot like a local American flight to somewhere in the Midwest. The plane was filled with middle-aged American businessmen equipped with black Lenovo laptops and baseball caps. The man next to me wore a cowboy-esque leather jacket over a blue-collared business shirt.


Prompt: After I landed in Atyrau’s single-gate airport, I located my driver, who was holding a card with my name on it. He swiftly led me into a seven-seater Mercedes van and drove me to my hotel, one of the only hotels in the city. Everyone from the flight also seemed to stay there. The drive was short. The city was overwhelmingly gray. Most of it was visibly poor. The hotel was an oasis of wealth.  Across from the hotel was another one of these oases: a gated community with beige bungalows. This was presumably where the expats who worked for Chevron lived. There was a Burger King and a KFC within walking distance. Everyone spoke a bit of English.

Response: Security was taken extremely seriously. Each time we entered one of Chevron’s offices, our passports were checked, our bags were inspected, and our bodies were patted down. Video cameras were mounted on the ceilings of the hallways and conference rooms. We were instructed to travel only using Chevron’s fleet of taxis, which were wired up with cameras and mics.


Prompt: All of this — Atyrau’s extreme security measures and the steady flow of American businesspeople — comes from the fact that the city is home to Kazakhstan’s biggest and most important oil extraction project. In 1993, shortly after the fall of the Soviet Union, the newly independent nation opened its borders to foreign investment. Kazakhstan’s state-owned energy company agreed to partner with Chevron in a joint venture to extract oil.  The project was named Tengizchevroil, or TCO for short, and it was granted an exclusive forty-year right to the Tengiz oil field near Atyrau. Tengiz carries roughly 26 billion barrels of oil, making it one of the largest fields in the world. Chevron has poured money into the joint venture with the goal of using new technology to increase oil production at the site. And I, a Microsoft engineer, was sent there to help.  Cloud Wars Despite the climate crisis that our planet faces, Big Oil is doubling down on fossil fuels. At over 30 billion barrels of crude oil a year, production has never been higher. Now, with the help of tech companies like Microsoft, oil companies are using cutting-edge technology to produce even more.  The collaboration between Big Tech and Big Oil might seem counterintuitive. Culturally, who could be further apart? Moreover, many tech companies portray themselves as leaders in corporate sustainability. They try to out-do each other in their support for green initiatives. But in reality, Big Tech and Big Oil are closely linked, and only getting closer.  The foundation of their partnership is the cloud. Cloud computing, like many of today’s online subscription services, is a way for companies to rent servers, as opposed to purchasing them. (This model is more specifically called the public cloud.) It’s like choosing to rent a movie on iTunes for $2.99 instead of buying the DVD for $14.99. In the old days, a company would have to run its website from a server that it bought and maintained itself. By using the cloud, that same company can outsource its infrastructure needs to a cloud provider.

Response: The market is dominated by Amazon’s cloud computing wing, Amazon Web Services (AWS), which now makes up more than half of all of Amazon’s operating income. AWS has grown fast: in 2014, its revenue was $4.6 billion; in 2019, it is set to surpass $36 billion. So many companies run on AWS that when one of its most popular services went down briefly in 2017, it felt like the entire internet stopped working.  Joining the cloud business late, Google and Microsoft are now playing catch-up. As cloud computing becomes widely adopted, Amazon’s competitors are doing whatever they can to grab market share. Over the past several years, Microsoft has reorganized its internal operations to prioritize its cloud business. It is now spending tens of billions of dollars every year on constructing new data centers around the planet. Meanwhile, Google CEO Sundar Pichai announced that in 2019, the company is putting $13 billion into constructing new offices and data centers in the US alone, the majority of which will go to the latter.


Prompt: Startups have long been the biggest early adopters of the public cloud. They are an obvious fit: they do not own their own data centers, so the opportunity cost of switching to the public cloud is low. By contrast, it is much harder for large companies that do run their own data centers to make the leap, since it would require selling or retiring those centers.  This helps explain why cloud providers have only captured about 30 percent of the total addressable market. While cloud technology has matured considerably over the past half-decade, big corporations that run their own data centers still dominate the majority of the world’s IT infrastructure. For Amazon, Google, and Microsoft, as well as a few smaller cloud competitors like Oracle and IBM, winning the IT spend of the Fortune 500 is where most of the money in the public cloud market will be made. And among those large companies, Big Oil sits at the top. Out of the biggest ten companies in the world by revenue, six are in the business of oil production. In order words, the success of Big Oil, and the production of fossil fuels, are key to winning the cloud race.  Making Friends In 2017, Chevron signed a seven-year deal with Microsoft, potentially worth hundreds of millions of dollars, to establish Microsoft as its primary cloud provider. Oil companies like Chevron are the perfect customer for cloud providers. For years, they have been generating enormous amounts of data about their oil wells. Chevron alone has thousands of oil wells around the world, and each well is covered with sensors that generate more than a terabyte of data per day. (A terabyte is 1,000 gigabytes.)  At best, Chevron has only been able to use a fraction of that data. One problem is the scale of computation required. Many servers are needed to perform the complex workloads capable of analyzing all of this data. As a result, computational needs may skyrocket — but then abruptly subside when the analysis is complete. These sharp fluctuations can put significant pressure on a company like Chevron. During spikes, their data centers lack capacity. During troughs, they sit idly.

Response: This is where the promise of the public cloud comes in. Oil companies can solve their computational woes by turning to the cloud’s renting model, which gives them as many servers as they need and allows them to pay only for what they use.  But Big Tech doesn’t just supply the infrastructure that enables oil companies to crunch their data. It also offers many of the analytical tools themselves. Cloud services provided by Microsoft, Amazon, and Google have the ability to process and analyze vast amounts of data. The tech giants are also leaders in artificial intelligence and machine learning (AI/ML), a field focused on teaching computer systems to automatically perform complex tasks by “learning” from data. With AI/ML, oil companies can make better sense of all the data they are collecting, and can discover patterns that may help them make their operations more efficient and less costly.  AI/ML gives Big Oil yet another reason to depend on Big Tech: the level of sophistication often requires delving into the cutting edge of a field that the tech titans dominate. And if sharing their AI/ML expertise means getting a leg up on the competition in the cloud market, tech companies are more than willing to help.  In recent years, Big Tech has aggressively marketed the transformative potential of the public cloud and AI/ML to Big Oil, with great success. In 2017, Microsoft signed its seven-year contract with Chevron; in 2018, it announced major partnerships with oil giants BP and Equinor; and in 2019, it signed a deal with ExxonMobil that Exxon claims is “the industry’s largest [contract] in cloud computing.” Amazon recently opened an AWS office in Houston, the US oil and gas hub, and has been hiring AI/ML experts specifically to work on fossil fuel projects. Google has also developed deep relationships in the industry, partnering with Total, Anadarko Petroleum, and Nine Energy, and appointing Darryl Willis, an oil veteran, to lead Google Cloud’s newly formed Oil, Gas & Energy division. Whatever the tech giants are telling their friends in the fossil fuel industry, it’s working.


Prompt: 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.

Response: 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.


Prompt: However, interpreting this map is a long and labor-intensive process. It can take months and involve many geophysicists. To make the process more efficient, computer vision technology can automatically segment different geological features to help geophysicists understand the 3D data and identify where best to drill. It seemed like a perfect example of the partnership I had been sent to Kazakhstan to help forge: a technically sophisticated and computationally intensive undertaking that played to the strengths of Big Tech while advancing a core priority of Big Oil, which was to dig more fossil fuels out of the ground while cutting costs.  Big Oil Is Watching But the TCO managers also wanted to talk about something else. “We have a lot of workers in the oil fields. It would be nice to know where they are and what they are doing,” one manager said. “If they are doing anything at all.” This is what our Chevron partners were most keen to discuss: how to better surveil their workers. TCO had thirty or forty thousand workers on site, nearly all local Kazakhstanis. They worked on rotating shifts — twelve-hour days for two weeks at a time — to keep the oil field running around the clock. And the managers wanted to use AI/ML to keep a closer eye on them.  They proposed using AI/ML to analyze the video streams from existing CCTV cameras to monitor workers throughout the oil field. In particular, they wanted to implement computer vision algorithms that could detect suspicious activity and then identify the worker engaging in that activity. (My Microsoft colleagues and I doubted the technical feasibility of this idea.) Enhancing workplace safety would be the reason for building this system, the managers claimed: more specifically, they hoped to see whether workers were drunk on site so that they could dispatch help and prevent them from hurting themselves. But in order to implement this safety measure, an “always-on” algorithmic monitoring system would have to be put in place — one that would also happen to give management a way to see whether workers were slacking off.

Response: The TCO managers also talked about using the data from the GPS trackers that were installed on all of the trucks used to transport equipment to the oil sites. They told us that the workers were not trustworthy. Drivers would purportedly steal equipment to sell in the black market. Using GPS data, the managers wanted to build a machine learning model to identify suspicious driving activity. It’s not a coincidence that minor tweaks to the same model would also allow management to monitor drivers’ productivity: tracking how frequently they took bathroom breaks, for example, or whether they were sticking to the fastest possible routes.


Prompt: The TCO managers were also interested in Microsoft products that could analyze large quantities of text. “Let’s say we have the ability to mine everyone’s emails,” one executive asked. “What information could we find?”  When I reflect back on this meeting, it was a surreal experience. Everyone present discussed the idea of building a workplace panopticon with complete normalcy. The TCO managers claimed that monitoring workers was necessary for keeping them safe, or to prevent them from stealing. But it wasn’t convincing in the slightest. We knew that they simply wanted a way to discipline their low-wage Kazakhstani workforce. We knew they wanted a way to squeeze as much work as they could from each worker.  I held my tongue and made sure to appear calm and collected. So did my colleagues. Collectively representing Microsoft, we turned a blind eye, and played along perfectly. We sympathized with TCO’s incriminating portrayal of their Kazakhstani workers and the need to uphold the rule of law. We accepted their explanation that increased surveillance would improve worker safety. But truth be told, we didn’t even need the excuses. Microsoft was hungry for their business. We were ready to concede.

Response: Skip the Straw The topic of worker surveillance took me by surprise. I didn’t sign up for it. I did sign up for helping to accelerate the climate crisis, however — and it was something I had thought about a lot by the time I landed in Atyrau.  When I was first asked to present at the workshop, I was excited. It was good for my career, the technology was fascinating, and I had never been to Kazakhstan. But I hesitated. Did I really want to help Chevron destroy the planet? There were others on my team who could have easily gone in my place. Still, I decided to go. I wanted to learn about the oil industry and the kinds of investments that Big Tech was making. I wanted a front-row seat to the Microsoft-Chevron partnership. I wanted to know what we were up against.  During the workshop, I asked a coworker how she felt about Microsoft working with Big Oil. She responded sympathetically, understanding my concerns about climate change. But she also seemed to feel there was nothing we could do. For her and many other colleagues I’ve spoken to, change has to happen at the top. The problem, of course, is that the top has powerful incentives not to change. Microsoft executives aren’t going to give up on the billions of dollars to be made from Big Oil, especially if it helps them win more of the coveted cloud market.


Prompt: 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.

Response: 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.


Prompt: While the country would struggle to take advantage of its oil-rich lands without the help of these foreign partners, the partnership is far from a win-win deal. Chevron keeps a tight grip on power, appointing most members of TCO’s upper ranks. The power dynamic was clear at the workshop: lower-level employees were Kazahkstanis while management was almost entirely American. The local economy has also completely aligned itself with the needs of the American-dominated TCO. TCO proudly announced in Q1 of 2019 that it spent over $1 billion on Kazakhstani goods and services, which includes hiring more than forty thousand local workers to work in the oil field. But this makes local businesses highly dependent on TCO. If American oil companies pulled out of the venture or slashed funding, TCO would crumble, and many businesses would lose their biggest (and often only) customer, leaving the economy in shambles.

Response: Big Tech isn’t responsible for Kazakhstan’s reliance on oil. Nor can we blame it for the climate catastrophe that we’re facing. But it is certainly exacerbating both. While Kazahkstan’s economy may benefit in the short run, intensifying the climate disaster will ultimately hurt the country too. Research shows that the region will suffer from increased aridity and more frequent heat waves, which could decrease crop yields and challenge food security.


Prompt: How can tech help, instead of hurt, the climate? How can tech companies make local economies more resilient rather than more vulnerable? How can we demand climate justice from Microsoft, a company that claims to be a leader in the fight against climate change? While I was in Atyrau, these very questions were being asked back home. Amazon employees in the US published an open letter calling on their company to reduce its carbon footprint and cancel its many contracts with Big Oil. Sitting in my hotel room not far from one of the largest oil fields in the world, I watched the letter blow up on my social media feeds. The number of Amazon signatures exploded: “3,500 employees challenge Bezos”, “4,200 Amazon workers push for climate action”, “6,000 employees sign an open letter to Bezos.” I was thrilled. Tech workers like me were taking a stand against our industry’s role in accelerating the climate crisis. They weren’t waiting for change at the top; they were demanding change from below.

Response: Then I thought of the young Kazakhstani engineer. What happens to people like him after we decarbonize? If Chevron and other oil giants cease operations, it would decimate the economy of places like Kazakhstan — places whose dependency on oil has been actively encouraged by those companies, which have in turn profited handsomely from it. Resource extraction is an ancient imperial practice. As tech workers join the movement for climate justice, we must also find ways to undo the legacies of Big Oil’s imperialism, and bring countries like Kazakhstan fairly and safely into a carbon-free future.


Prompt: But it won’t be easy. When I returned to the US, I learned that Bezos had effectively ignored the demands of over 8,000 of his employees. The open letter was an important first step, but more action will be needed for Amazon to drop its oil partnerships. We have a long fight ahead of us, and the stakes are high. We have, quite literally, a world to win.

Response: For a long time, a certain set of assumptions dominated our digital imagination. These assumptions should be familiar enough. Information wants to be free. Anything that connects people is good. The government is bad. The internet is another world, where the old rules don’t apply. The internet is a place of individual freedom, which is above all the freedom to express oneself.  Such ideas were never 100 percent hegemonic, of course. They were always contested, with varying degrees of success. Governments, for one, found several ways to assert their sovereignty over online spaces. Scholars sounded the alarm on the rise of the white supremacist web — the notorious neo-Nazi site Stormfront launched in 1996 — and presciently observed that the internet’s connectivity could also make the world worse.


Prompt: Even so, these assumptions and the intellectual traditions they emanated from — techno-utopianism, cyberlibertarianism, the Californian Ideology — largely kept their grip on the common sense. The long 1990s is said to have begun with the fall of the Berlin Wall in 1989 and ended with the attacks of September 11, 2001. But, when it came to our popular discourse about the internet, the long 1990s lasted a lot longer.  Then came Snowden. In 2013, the former NSA contractor revealed that the internet was a vast spy machine for the American security state. A tremor of tech pessimism crept into public consciousness. Then came Trump. The media’s failure to anticipate the possibility of his victory in 2016 led it to amplify the significance of Russian influence operations via social media — operations that clearly existed, but which, at a moment of supreme disorientation, metastasized into the deus ex machina that could explain an inexplicable result. Yet this coping mechanism had a silver lining: it provided the initial spark for what has come to be known as the “techlash.” Journalists and politicians began to pay closer, less credulous attention to the internet and the companies that control it. Disinformation remained a key concern, but far from the only one: a long series of tech scandals have fed the fire, too many to keep count. The right has also joined the fray: the (laughable) notion that the big platforms silence conservative voices has taken root in the reactionary mind, turning a range of right-wing figures into harsh critics of Silicon Valley.

Response: The resulting shift is stark. A sharper tone prevails in the New York Times and on Fox News, in statehouses and on Capitol Hill. Criticisms once confined to scholarly circles, or to more oppositional outlets like The Baffler and Valleywag, have become conventional, even banal. One could be uncharitable about the heavy Kool-Aid drinkers who abruptly sobered up — there is no shortage of annoying figures among the late converts to tech critique — but the techlash has been a very good thing. We are at last having a more honest conversation about the internet. The long 1990s are over. The old gods are finally dead.  Who are the new gods? This is what makes our moment so interesting: the conventional wisdom is cracking up but its replacement hasn’t quite consolidated. As James Bridle says, something is wrong on the internet — and something is wrong with the way we have thought about the internet — but there is not yet a widely accepted set of answers to the all-important questions of why these things are wrong, or how to make them right.  Different camps are now competing to provide those answers. They are competing to tell a new story about the internet, one that can explain the origins of our present crisis and offer a roadmap for moving past it. Some talk about monopoly and antitrust. Others emphasize privacy and consent. Shoshana Zuboff proposes the term “surveillance capitalism” to describe the new kinds of for-profit monitoring and manipulation that the internet and associated technologies have made possible.  These analyses have important differences. But they tend to share a liberal understanding of capitalism as a basically beneficent system, if one that occasionally needs state intervention to mitigate its excesses. They also tend to equate capitalism with markets. Sometimes these markets become too consolidated and need to be made more competitive (the antitrust view); sometimes market actors violate the terms of fair exchange and need to be restrained (Zuboff’s view). But two articles of faith always remain. The first is that capitalism is more or less compatible with people’s desire for dignity and self-determination (or can be made so with proper regulation). The second is that capitalism is more or less the same thing as markets.


Prompt: What if neither belief is true? This is the starting point for building a better story about the internet. The Archipelago and the Network If capitalism isn’t (only) markets, then what is it? There have always been markets. Capitalism, by contrast, is relatively new. Its laws of motion first emerged in Europe in the fifteenth and sixteenth centuries, and reached escape velocity with industrialization in the eighteenth and nineteenth.

Response: If capitalism didn’t invent markets, however, it did make markets much more important. The historian Robert Brenner observes that capitalism is defined above all by market dependence. Pre-capitalist peasants can trade and barter, but they don’t depend on the market for life’s necessities: they grow their own food. In capitalist societies, on the other hand, the market mediates your access to the means of subsistence. You must buy what you need to survive, and to have the money to do so, you must sell your labor power for a wage.  Market dependence doesn’t exist for its own sake. It serves an important function: to facilitate accumulation. Accumulation is the aim of any capitalist arrangement: to take a sum of value and make more value out of it. While markets are certainly central to capitalism, they aren’t what makes it tick. Accumulation is. To put it in a more Marxist idiom, capital is value in motion. As it moves, it expands. Capitalism, then, is a way to organize human societies for the purpose of making capital move.  There are a few different methods for making capital move. The principal one is for capitalists to purchase people’s labor power, use it to create new value in the form of commodities, and then realize that value as profit by selling those commodities. A portion of the proceeds are reinvested into expanding production, so even more commodities can be made at lower cost, thus enabling our capitalist to compete effectively with the other capitalists selling the same commodities.  This may seem entirely obvious, but it’s actually a very distinctive way of doing things. In other modes of social organization, the point of production is to directly fulfill people’s needs: think of subsistence farmers, growing food for their families to eat. Or the point is to make the rulers rich: think of the slaves of ancient Rome, doing the dirty work so that imperial elites could lead lives of luxury.  What makes capitalism so unusual is that production (and accumulation) isn’t for anything exactly, aside from making it possible to produce (and accumulate) more. This obsession gives capitalism its extraordinary dynamism, and its revolutionary force. It utterly transforms how humans live and, above all, how they produce. Capitalism forces people to produce together, in increasingly complex combinations of labor. Production is no longer solitary, but social.


Prompt: This dynamic is most vividly illustrated by the factory. The modern factory was largely born in nineteenth-century Manchester, where Friedrich Engels’s father co-owned a cotton mill. This gave the young Engels the opportunity to observe the birth of the factory up close. He saw hundreds, even thousands of workers, crammed into vast buildings, arrayed around machines, and performing different roles within a complex division of labor in order to work as one. What they made, they made together.  In pre-capitalist Europe, one person or a few people could plausibly claim credit for producing something. This wasn’t the case in the capitalist factory, however. “The yarn, the cloth, the metal articles that now come out of the factory were the joint product of many workers, through whose hands they had successively to pass before they were ready,” Engels wrote. “No one person could say of them: ‘I made that; this is my product.’” Yet there was a contradiction lurking here. If no one worker could claim sole credit for a product, the owner of the factory could still claim sole ownership of everything the workers made together. Wealth was being created socially, on a new model — but still owned privately, on the old model.  The contradiction became even sharper when zooming out to consider the wider economy. As many workers as it took to run a Manchester mill, it took even more workers to make that work possible, from the machinists who manufactured and maintained the power looms and the other machines to the slaves in the American South who picked the cotton that kept those machines fed. The collective labor inside the mill was sustained by many concentric circles of collective labor outside of it.

Response: The pre-capitalist economy looked like a cluster of islands — an archipelago. It involved a collection of small producers relatively isolated from one another and producing mostly for personal use. (Marx memorably compared the French peasantry to a sack of potatoes.) By contrast, the capitalist economy looked like a network. The network of capital concentrated masses of people into larger nodes of production and linked them through countless threads of interdependence. Yet the wealth that this network generated didn’t flow to the many workers who collectively created that wealth. It flowed to the few who owned the network: the capitalists.


Prompt: Before capitalism, when production happened on a more personal basis, such an arrangement might’ve made sense. If the economy was a cluster of islands, it followed that each island would own what it made. But capitalism, by revolutionizing production, introduced a contradiction: wealth was now made as a network, but still owned as an archipelago. Capitalists like Engels’ father became rich. The workers of Manchester earned starvation wages, and lived in cholera-infested slums.

Response: The New Manchester What does this have to do with the internet?  The internet, and the constellation of digital technologies that we call “tech” more broadly, intensifies the fundamental contradiction in capitalism between wealth being collectively produced and privately owned. It takes the Manchester model and elevates it to the nth degree. It makes the creation of wealth more collective than ever before, piling up vast new fortunes in the process — fortunes that, as they did in Engels’ day, accrue to a small handful of owners.  A worker in a Manchester mill couldn’t point to a finished piece of yarn and say, “I made that,” but a few thousand workers (and slaves) probably could. Tech’s wealth, on the other hand, is woven out of the contributions of billions of people, living and dead.  This helps explain why the tech industry is so ludicrously profitable. Take Facebook. In 2018, Facebook reported a net income of $22 billion with an operating margin of 45 percent. The company only has about 40,000 full-time employees, along with an undisclosed number of contractors. In other words, relative to its costs, Facebook makes an absurd amount of money. And Facebook’s power isn’t just about money: as the dominant media ecosystem in many countries around the world, it also embodies what Frank Pasquale calls “functional sovereignty.” It operates like a government — which is particularly evident in the case of Libra, its new global digital currency. And this government is quite explicitly autocratic given a shareholder structure that preserves Mark Zuckerberg’s personal control of the company.  It’s hard to imagine a more extreme form of the contradiction on display at Manchester than a social network of more than two billion people ruled by a single billionaire. The network of capital has become denser, and more literal, than Engels could’ve possibly imagined, while its control has become concentrated in even fewer hands.


Prompt: 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.

Response: 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.


Prompt: As varied as these jobs are, though, they still look like traditional labor. People work and get paid, whether they’re inventing the internet protocols or laying fiber-optic cable. Tech, however, also manages to draw value from activities that don’t look like traditional labor. To return to Facebook, those more than two billion users create value for the company by supplying the site with its posts, comments, and likes. This content, paired with the rest of their activity on the platform, also furnishes Facebook with the personal data it uses to sell targeted advertising, which makes up the vast majority of its revenue.  It’s a contested theoretical question whether all this posting and clicking should count as “labor” — and if so, what kind. In her canonical article on the subject, the theorist Tiziana Terranova uses the term “free labor” to describe the various unwaged activities that propped up profit-making in the early days of the commercial internet, from volunteer moderators on America Online to open-source software developers. But the scope of such activities has grown dramatically since Terranova published her piece in 2000, and they look less and less like labor. Increasingly, tech is able to harvest value from us simply for existing.  A good example comes in the form of a cafe in San Francisco called Brainwash. This cafe, since closed, had a camera inside of it that filmed customers. A group of researchers obtained the footage, and turned it into a dataset to train machine learning models for detecting heads and faces. Published in 2016, this dataset was then used by the Chinese firm Megvii, a global leader in facial recognition, to refine its own software. Megvii also happens to be implicated in the totalitarian surveillance state that the Chinese Communist Party is constructing in the western province of Xinjiang. In other words, by walking into a cafe one day in San Francisco, you may have helped a tech company make money by selling the Chinese government a product it uses to repress millions of its citizens some six thousand miles away. (Megvii is currently valued at $4 billion, and hopes to raise as much as $1 billion in an IPO expected for late 2019.)

Response: These kinds of strange and tangled value chains will only become more common in the coming years. As small networked computers burrow ever more deeply into our homes, stores, streets, and workplaces, more data will be made. Meanwhile, advances in machine learning and the growth of cloud-based processing power will continue to make data more valuable, as the fuel that feeds automated systems for everything from recognizing faces to predicting consumer preferences.  The upshot is a world where the creation of wealth becomes more collective than ever before. In the nineteenth century, Engels reflected on how capitalism transformed production “from a series of individual into a series of social acts.” The total enclosure of our world by computing means that those social acts can now happen at the scale of entire societies. The industrial factory has become what Terranova and others, building on a term from Italian autonomism, call the “social factory.” The new Manchester is everywhere.


Prompt: The Difference Engine Capitalism connects. In its perpetual push to accumulate, it draws people into new sites and circuits of collective wealth-making. But if capitalism is a connector, it is also a differentiator. If capitalism is a network for making wealth, it is also an engine for making difference.

Response: To watch this differentiating dynamic at work, let’s return to Manchester for a moment. The people who collectively created the city’s wealth were not a single homogenous mass. Quite the opposite: they were divided into men and women, English and Irish, white and Black. And these divisions were constantly being reinforced, since they served a valuable purpose: they helped make exploitation seem justified, even natural.


Prompt: Thus it was natural for the Irish to be paid less and live in appalling slums. It was natural for women to be paid less while also performing the unpaid work of raising children — children who went into the mills as young as five. It was natural to enslave human beings of African origin and put them to work harvesting the cotton that those mills turned into textiles. It was natural to dispossess and exterminate the Indigenous people who had formerly inhabited the land that became those cotton fields.  Capitalism doesn’t invent human difference, of course. Humans look different; they speak different languages; they come from different communities and cultures. But capitalism makes these differences make more of a difference to people’s lives. Differences become more differential. They become differences of capacity and value — differences in how much a human being is worth, or if they’re even considered human at all.  The political scientist Cedric J. Robinson argued that this difference-making has been a core feature of capitalism from the beginning — he called it racial capitalism for this reason. Feudal Europe was highly racialized, Robinson said. As Europeans conquered and colonized one another, they came up with ideas about racial difference in order to justify why, for instance, Slavs should be slaves. (In fact, Slavs were so frequently enslaved in the Middle Ages that they supplied the source of the word “slave,” in English and several other European languages.) If racial thinking saturated the societies where capitalism first emerged, capitalism subsequently picked up these concepts and extended them. It generated deeper and more varied ideas about racial difference in order to justify the new relationships of domination that the imperative of accumulation demanded — particularly as Europeans began carving up Asia, Africa, and the Americas. “The tendency of European civilization through capitalism,” Robinson wrote, “was thus not to homogenize but to differentiate — to exaggerate regional, subcultural, and dialectical differences into ‘racial’ ones.”

Response: Robinson’s insight helps clarify another crucial aspect of how tech operates. If tech intensifies capitalism’s contradiction between wealth being collectively produced and privately owned, it also intensifies capitalism’s tendency to slice people into different groups and assign them different capacities and values. Indeed, the two operations are closely related. “Capital can only be capital when it is accumulating,” says the theorist Jodi Melamed, “and it can only accumulate by producing and moving through relations of severe inequality among human groups.” The network for making wealth, in other words, relies on the engine for making difference.  That engine is now made of software. Differentiation happens at an algorithmic level. The abundant data that flows from mass digitization, combined with the ability of machine learning algorithms to find patterns in that data, has given capitalism vastly more powerful tools for segmenting and sorting humanity.


Prompt: Way back in 1993, the media scholar Oscar H. Gandy, Jr. offered an extremely prescient view of how this works. He called it “the panoptic sort,” in a book of the same name. “The panoptic sort is a difference machine that sorts individuals into categories and classes on the basis of routine measurements,” he wrote. “It is a discriminatory technology that allocates options and opportunities on the basis of those measures and the administrative models that they inform.” Gandy was looking at how corporations and governments collected and processed personal information at a time when computing was widespread, but fairly primitive by today’s standards — the commercial internet was still years away. Even so, Gandy discerned a logic that by now feels very familiar. Data was being drawn from many sources — thus the “panoptic” part — in order to sort people “according to their presumed economic or political value.” And this operation wasn’t peripheral or incidental to capitalism, but absolutely integral to it: the panoptic sort, Gandy argued, was “the all-seeing eye of the difference machine that guides the global capitalist system.” Today, this all-seeing eye sees much, much more. And the stakes of the sorting are even higher. Algorithmic differentiation helps determine who gets a loan, who gets a job, who goes to jail. Moreover, Gandy observed how the panoptic sort amplified existing disparities, racial and otherwise. This is far truer today, thanks to the mainstreaming of machine learning systems.

Response: In recent years, scholars and journalists have called attention to the problem of “algorithmic bias.” Such bias is endemic to machine learning because it “learns” by training on data drawn from our social world — data that inevitably reflects centuries of capitalist difference-making. Thus “predictive policing” algorithms trained on data that shows that the police arrest a lot of Black people suggest arresting more Black people. Or an Amazon algorithm trained on the resumes of its mostly male workforce advises against hiring women.


Prompt: The role of these systems is not just to reproduce inequalities, but to naturalize them. Capitalist difference-making has always required a substantial amount of ideological labor to sustain it. For hundreds of years, philosophers and priests and scientists and statesmen have had to keep saying, over and over, that some people really are less human than others — that some people deserve to have their land taken, or their freedom, or their bodies ruled over or used up, or their lives or labor devalued. These ideas do not sprout and spread spontaneously. They must be very deliberately transmitted over time and space, across generations and continents. They must be taught in schools and churches, embodied in laws and practices, enforced in the home and on the street.

Response: It takes a lot of work. Machine learning systems help automate that work. They leverage the supposed authority and neutrality of computers to make the differences generated by capitalism look like differences generated by nature. Because a computer is saying that Black people commit more crime or that women can’t be software engineers, it must be true. To paraphrase one right-wing commentator, algorithms are just math, and math can’t be racist. Thus machine learning comes to automate not only the production of inequality but its rationalization.


Prompt: The New Barcelona Anything that moves has an ideal medium for its motion. A fish moves best in water; a car moves best on pavement. Capital is value in motion, so it must always be moving. And it moves best through a particular kind of social fabric, one that is both webbed and fissured, linked and sliced, connected and differentiated.

Response: This helps make sense of what we call tech. Tech is an agent and accelerant of these dynamics, of “densely connected social separateness,” to borrow a term from Melamed. This explains its tendency to generate immense imbalances of wealth and power, and to heighten the hierarchical sorting of human beings according to race, gender, and other categories.


Prompt: For our analysis to be useful, though, it needs to have not only a descriptive but a prescriptive element. It needs to offer some answers to the question of what is to be done.  This is where things get murkier, as one might expect. But there is clarity on at least one point. If tech intensifies the contradiction between wealth being made by the many and owned by the few, then the obvious solution is to resolve the contradiction: to turn socially made wealth into socially owned wealth. Or, as Marx and Engels put it in The Communist Manifesto, to convert the “collective product” of capital into “common property, into the property of all members of society.”  The logic is appealingly simple: if the network makes the wealth, then let the network own the wealth. But how, precisely? What does it mean to transform the wealth that society makes in common into the common property of society? This is the most bitterly debated question in the whole history of the radical Left. For most of the actually existing socialisms of the twentieth century, the answer was full nationalization on the Soviet model. This answer hasn’t aged well.  Another approach, and one that is currently enjoying renewed popularity, draws from the tradition of worker self-management. This tradition comes in many flavors, but perhaps its most heroic moment occurred in revolutionary Catalonia during the Spanish Civil War, when people seized factories, farms, even flower shops and, for a brief period, ran everything themselves. A young Marxist from Kentucky named Lois Orr would later remember the thrill of strolling through anarchist Barcelona and seeing its “cafés, restaurants, hotels, and theaters lit up red or red and black [with] banners saying Confiscated, Collectivized.”  Barcelona, then, is one alternative to Manchester. But what would self-management mean for tech? A number of different experiments offer preliminary materials towards an answer. There are small, cooperatively owned platforms for everything from ride-hailing to social media. There are municipally owned broadband networks governed by local communities. There is an initiative to create a socially owned smart city in, of all places, Barcelona. There are also more ambitious but less immediately feasible schemes for democratizing the big platforms, whether by converting them into cooperatives of some kind or socializing their data.

Response: These projects and proposals have the virtue of being concrete. As working hypotheses, they are immensely valuable. But they remain necessarily incomplete and provisional, particularly when considered as possible directions for moving beyond capitalism. Cooperatives under capitalism often behave like normal firms, since they are subject to the same market imperatives as everyone else. There is no straight line, then, from experiments in self-management to the broader goal of breaking with the logic of infinite accumulation and rebuilding society on a radically different basis.  Neither is there a direct relationship between democratizing ownership and combating the various oppressions implicated in capitalist difference-making. A cooperatively owned platform wouldn’t put an end to algorithmic racism, for instance. This brings us to another important point: sometimes the most emancipatory option isn’t to transform how infrastructures are owned and organized, but to dismantle them entirely.


Prompt: Thinking in Motion Consider the Stop LAPD Spying Coalition, an alliance that has been organizing against police surveillance in Los Angeles for years. They have successfully pushed the LAPD to abandon two predictive policing programs — programs that led to increased police violence against working-class communities of color. The organizers did not want these programs reformed, but stopped. They were not demanding that the ownership of the algorithmic policing apparatus be “democratized,” whatever that might mean, but abolished.  Here is an organization that is taking on tech’s tendency to intensify capitalist difference-making, and using the framework of abolition to do so. One can see a similar approach in the emerging movement against facial recognition, as some city governments ban public agencies from using the software. Such campaigns are guided by the belief that certain technologies are too dangerous to exist. They suggest that one solution to what Gandy called the “panoptic sort” is to smash the tools that enable such sorting to take place.

Response: We might call this the Luddite option, and it’s an essential component of any democratic future. The historian David F. Noble once wrote about the importance of perceiving technology “in the present tense.” He praised the Luddites for this reason: the Luddites destroyed textile machinery in nineteenth-century England because they recognized the threat that it posed to their livelihood. They didn’t buy into the gospel of technological progress that instructed them to patiently await a better future; rather, they saw what certain technologies were doing to them in the present tense, and took action to stop them. They weren’t against technology in the abstract. They were against the relationships of domination that particular technologies enacted. By dismantling those technologies, they also dismantled those relationships — and forced the creation of new ones, from below.


Prompt: Machine-breaking is often a good idea; for more ideas, we can turn to other movements. Tech workers are taking collective action against contracts with the Pentagon and ICE, and demanding an end to gendered discrimination and harassment. Gig workers for platforms like Uber are organizing for better wages, benefits, and working conditions. Within these movements we can find more useful materials to think with, materials that might disclose the contours of a society organized along different lines.  The intellectual is not the only one who thinks. Masses of people in motion also think. And it is the thinking of these two together, in the creativity that results from their continuous interaction, that furnishes the form and content of anything worth calling socialism. This process is messy and circuitous, with many blind alleys and false starts. It involves more time spent moving contradictions around, and creating new ones, than resolving them. But it is the only path to a future where capital’s motion finally grinds to a halt, and a different set of considerations — human need, a habitable planet — comes to coordinate our common life. This is how the Left will answer the question of what is to be done, about tech and about everything else: by thinking en masse and thinking in motion, while traversing difficult terrain.

Response: “It’s kinda like the Parthenon now, it’s a testament to something…” —Nick Bongiorno, former IBM temp worker and activist  The IBM country club on a hill overlooking Endicott, New York has been empty for thirteen years. Now beyond repair, it was once abuzz with the activity of some 14,000 IBMers and their families. There were basketball games, swimming pools, a bar, a stage, banquet halls, guest rooms, and a golf course, all open to the thousands of IBM employees in Endicott. This was the town where, in 1911, International Business Machines was born.  IBM’s Plant Number One manufactured punch-card tabulators in downtown Endicott. Then came typewriters, printers, and the System/360 computer, after which a parade of ever-newer models were made. For decades, IBM dominated the computer industry. It was not until 1996 that their market value was surpassed by Microsoft. They have since fallen far down the ladder: these days, IBM is only the ninth-largest tech company in the world. They have gotten out of the messy business of making things and are now, primarily, a software and services company. In 2002, after more than ninety years, IBM ceased manufacturing in Endicott.


Prompt: Today, the IBM country club is utterly defaced. Defaced by the freeze and thaw of water, and the flooding of the Susquehanna River. Defaced by teenagers cycling through in the night, the keepers of no-man’s land. This is where they come to get high and destroy the memory of IBM, their father’s house. Graffiti on an unbroken window says Did You Ever Love Me? in searing pink paint.          Live         Die         Suck Me Softly, it says.

Response: In the old brick rooming house, white columns have fallen against the floor. The fireplace is stuffed with saplings and branches; it looks wild and evil. Stiff palatial curtains remain. There are pellets of rat shit on the ground and moss grows across the maroon carpeting. The pink and green wallpaper peels and blooms like wild roses. On the floor is a crumpled up brochure from 1954. “IBM Family Day” for “Plant Employees,” it says, on a placard held up by a smiling clown. After pie-eating contests and a softball game, at 6 p.m. the IBM band would play.  The bathroom in the old IBM country club. Photo courtesy of the author.


Prompt: In 2006, the Susquehanna River rose through the rooms, easily ripping at their seams and fixtures, a flowing push against the old giant. Named “Oyster River” by the Lenape people, the Susquehanna is one of the country’s most polluted. It came to know nuclear meltdown from Three Mile Island, and it knew coal and fertilizer and feces, before it came into the club. Then the teenagers charged against the broken surfaces.  In the locker room there are still four inches of green soap in the dispenser but the porcelain toilets are smashed or filled with shit. The mirrors have upside-down crosses and         666         PUSSY         SATAN  With language learned from horror movies, the trespassers hack away at Big Blue’s shadow. A fuck you, from the town to IBM. A mutual fuck you, from IBM to the town.

Response: The Plume It started beneath a swath of train tracks and poured gravel beside Building 18, where circuit boards were made. It formed from repeated spills of volatile organic compounds used in the degreasing and cleaning of microchips. In 1978, 1.75 million gallons of wastewater were released. That same year, 4,100 gallons of liquid solvents, including trichloroethylene (TCE) and trichloroacetic acid (TCA), were released. In 1980, IBM contacted the Environmental Protection Agency (EPA) to report that contaminants had begun to form a pool beneath Building 18. This is what became known as the plume.  It is hard for people to believe in invisible things, but the plume began to manifest. Its vapors started to travel underground. It spread to encompass 300 acres of the town: churches, movie theaters, grocery stores, and 480 homes. It was no longer invisible when people began to get sick.


Prompt: The danger of TCE exposure is that it is carcinogenic and can impair fetal development. The chemical penetrated deep into the groundwater as a liquid and then began to evaporate, moving through air pockets in the soil. This migration continued through cracks in the foundations of homes and buildings, creating an indoor environment of prolonged exposure. People who both lived and worked in the plume were called “double dippers.”  When the initial spill occurred, IBM began digging wells. Twenty extraction wells pumped out contaminated groundwater. In 2002, the year IBM shuttered its factories, the New York State Department of Environmental Conservation (DEC) required the company to test the air quality. By 2004, they entered into a “formal consent order” to investigate and remediate the contamination. What IBM found led them to install vapor mitigation systems in homes and other buildings in the plume.  These systems are discernible by the white boxes attached to long pipes that reach up to roofs, rerouting the vapor from underground into the surrounding air outside. Now in homes, houses of worship, billiard bars, and barber shops, there is a constant whir of ninety-watt motors working against TCE. The contamination is continually announcing itself. It is ignorable as a low drone, forgotten and re-heard over and over again.  Endicott is important because it is not unique. It is a story that one can almost write without knowing the specifics. It is a story of the postindustrial long after the last shoe, car, or computer travels through the factory.  The old IBM complex in Endicott. Photo courtesy of the author.

Response: Endicott proves that it is only through extraction, refinement, and manufacturing that computational feats of any kind are possible. The machine is made of materials from the earth: copper, gold, nickel, silicon. In order to purify, clean, and combine its pieces, intensive chemical baths are used. The computers and smartphones that result have an incredibly short working life, on average just two to three years. A shorter life than a car tire, a winter coat, a stereo, or a shovel.  Though compact when presented to consumers, these devices also have a huge material footprint. The inputs for a microchip are 630 times the mass of the final product. After the product is made, all of these excess inputs recombine into new chemical slurries, the unsaleable byproduct of the machine. These life-altering chemicals return to the earth in indigestible ways, and creep through our basements, waterways, genomes. There are 2.71 billion smartphone users in the world, and 1.5 billion personal computers in use. This means there are many towns like Endicott.  Inside the Clean Room I too am from an IBM town, one in northern Vermont, the only in the state. Like thousands of other Vermonters, I worked in the factory there. I didn’t get sick and neither did my immediate coworkers, but I began to hear troubling stories. I also began to read article after article imploring IBM to stay in Vermont. Eventually IBM did leave, but unlike in Endicott, the factory was taken over by a new company and kept running. In Vermont the pollution was quieter. The factory was not classified as a Superfund site, so it did not stick in the public conscience — only in the thoughts of those who worked there. The pollution also remained quieter because the factory is still in operation.  The work itself, twelve-hour shifts in a factory built to protect the product not the people, was dehumanizing. I performed one step in hundreds required to make microchips. The Vermont plant specializes in amplification chips that transmit signals to satellites and enhance the speakers nestled next to our ears on our phones. Twenty-four hours a day, white-clad employees walk up and down the fluorescent hallways of the factory: workers in hoods, gloves, veils, booties, and coveralls so that the eyes are all that is visible. This is to protect the delicate chip from human contaminate.


Prompt: I worked in the “wets” department, applying chemical cleaners to microchips so that layers of circuitry can be built cleanly on top of one another. The chemical wash machines I operated look similar to a home washing machine. The workers used to man the chemical baths themselves, balancing the boxes of wafers on a hooked pole and removing them when a manual timer dinged. Some even used to volunteer for this job because they thought it could get you high. Now the process is more mechanized: I performed the simplified and repetitive function of loading and unloading the chemical wash machine, then putting the product onto an automated overhead track, so that there were always half-made microchips floating above, traveling to the next tool required to complete them.  The pay is a few dollars above minimum wage. I had to pass background and literacy tests and formally agree to chemical exposure in my job interview. I’m not sure whether it was the low pay, volatile hiring and firing, or the sensory deprivation inherent in working in a “clean room,” but the workers engaged in small rebellions. People had sex behind the robots, got loaded on their lunch breaks, defecated in trash cans, and hid six-packs in the floor tiles. One janitor, tasked with cleaning the factory, called in a bomb threat.  The Vermont factory, like the one in Endicott, spilled chemicals into the surrounding area. Five miles of underground piping leaked, delivery drivers spilled solvents, workers poured waste into drains that were not hooked up to pipes, the contents of unsealed wells and a “sludge landfill” seeped into the earth. The chemicals spread forty acres wide and travelled 300 acres deep into the bedrock of the town.  In 1979, the contamination came to light when IBM reported the spills to the EPA. TCE and PCE, another carcinogenic degreasing agent, were found in the nearby river and lake: in some areas, PCE levels were 19,000 times higher than the state standard allowed in drinking water. In cooperation with the EPA, IBM began a groundwater cleanup campaign. Contractors for the company in Vermont said the process, conducted across six sites, would take more than two hundred years. Scientists say it could take thousands.

Response: In Endicott, the New York State DEC does not have a time estimate at all. In both cases, the contamination will cling to the land long past the lifespan of a factory, a product, or even an industry. Sacrifice Zone When IBM left Endicott there was no new company to take over the factory, like in Vermont. In the newly quiet town, IBM’s legacy began to ring louder. It was not simply the land that was changed, but the people too.  In Endicott, both teenagers and adults got cancer. One girl broke her leg walking through the halls of her high school. At the hospital she found out she had bone cancer. And there were children born with malformed hearts. The New York State Department of Health (DOH) reported fifteen cases of infants born with heart defects over seventeen years in one Endicott neighborhood. This number is more than twice that of the normal population.  When Kevin Every moved to a rental in Endicott from Philadelphia, his wife Tiah was pregnant with their youngest. If he had known that the house was in the plume he never would have rented it. “But nobody gave me that choice,” he said. Instead, he found out about the plume on the news. When his son Deron was born, he had six different heart defects. At thirteen days old, he had his first operation. At eight years old, he had a stroke.  “He prays,” Kevin told me. Deron prayed for a new heart, and got a transplant this summer. The family eventually bought a house outside the plume. Kevin doesn’t know if ventilation was ever installed on his rental or who lives there now. Renters, who don’t know the history of the area or can’t afford higher rents, are unfairly affected. They are often transient enough not to be accounted for when they get sick. At the Ronald McDonald House in Syracuse, when Deron was an infant recovering from his initial heart surgery, Kevin met seven families. They were his neighbors from Endicott. Their babies also had heart defects. When Kevin got back to Endicott, he called a lawyer.


Prompt: Today, Kevin spends a lot of time traveling to doctors. He tells his four other kids, “If that was you, I’d be doing it for you.” I asked Kevin about IBM: if he thinks it’s an accident, if he thinks they’re sorry. “They can do whatever they want just so they can have a buck… families lose because now they have a loved one that’s sick. If I went out and changed the oil in my car and dumped it on the grass I would get in trouble,” he said. When a commercial for IBM comes on TV, he can’t bear to watch it. “This is happening all around America,” he said. No matter if they ever admit what they’ve done, “right is right and wrong is wrong.” James Little worked at IBM in Endicott for fifteen years as a senior operator making chip boards. He worried about leaky machines. Once, he shut down a machine that was spilling chemicals into an overflow tray. His manager chastised him and told him never to do that again. James heard rumors of chemicals dumped in holes in the concrete cellar, pipes with leaks, and train cars that spilled their deliveries of chemicals. Workers around him were getting sick. A girl who worked beside him got a brain tumor. A man in his department had his nostrils “eaten out” from the fumes. “The bottom line,” Little said “was they wanted to get the work out… I think people were sacrificed.” Little became an activist and workplace safety advocate. He talked to the press. His manager told him if his name appeared in the paper one more time he would be fired. He kept his job until the factory shut down.

Response: Such stories aren’t limited to Endicott. There are similar stories wherever IBM manufactured chips. Michael Ruffing and Faye Carlton worked at IBM in East Fishkill, New York. They sued IBM after their son was born blind with facial deformities that prevented him from breathing normally. Candace Curtis, whose mother worked while pregnant with her in the same East Fishkill plant, was born without kneecaps. She is not physically capable of talking. Nancy LaCroix, of IBM Vermont, had a baby girl with bone defects, which caused her brain to protrude from her skull and left her with stunted fingers and no substantial toes. One unnamed child of an employee was born without a vagina.


Prompt: Superfund Site, IBM on Trial Beginning in the 1990s, lawsuits began popping up all over the country where IBM made chips. A case from IBM San Jose that sought to establish a cancer link with chemical exposure in factories was dismissed when, after two days, the jury decided in favor of IBM. More than 200 workers in Vermont, New York, Minnesota, and California brought lawsuits against IBM for work and resultant environmental conditions that caused them or their children to become ill. All settled out of court.

Response: In 2008, a group of around 1,000 Endicott residents sued IBM for $100 million over increased occurrence of kidney cancer, heart defects in children, and lowered property values. In proceedings, IBM was forced to disclose the contents of their “Corporate Mortality File,” a database dating back to 1969, a decade after the invention of the microchip. IBM claimed the file was created to track pensions and other lasting benefits to the families of deceased workers. It contained 33,730 former employees with basic identifiers like sex, age, work history, and, most importantly, cause of death. Increased rates of respiratory and breast cancer as well as cancer in the internal organs were found.  After seven years and no trial, IBM eventually settled the case out of court for an estimated $13 million. No wrongdoing was publicly admitted and no cancer link credibly established. “These are tragic cases, but there is no scientific evidence that there are increased rates of diseases of any kind among IBM employees,” an IBM spokesman stated in response to the rash of lawsuits. Kevin Every’s family was part of the $13 million settlement. He can’t go into detail but said, “We didn’t get what we shoulda got. They asked me how much I think we should get, I said everything. [Deron] can’t even go on the playground.”  Although IBM has denied responsibility for the health problems in Endicott, they have committed to helping clean up the town. (They have also tried to burnish their public image and defuse anger among residents with philanthropy: in 2002, on the day Endicott was classified as a class 2 Superfund site by the EPA, IBM gave Endicott a “gift” of $2 million.) The plume has shrunk considerably since remediation efforts began pumping out contaminated groundwater. A smaller plume means less toxic vapor intrusion into local homes and businesses. For now, the white vapor mitigation boxes on the outside of houses remain and the groundwater pumps continue to suck up polluted water.  Fortunately, IBM is a rich company with plenty of money for remediation. For IBM, spending $270 million on environmental clean-up projects across the nation in 2017 was easily absorbed in the following year’s revenue of $80 billion.


Prompt: James Little, the former factory worker, hopes that new business will come to Endicott now that it is cleaner. He still loves his hometown. “I would consider this site pretty much safe,” Little told me, but he knows the spilled chemicals will be nearly impossible to totally eradicate. They will continue to use the vapor mitigation systems and the groundwater pumps.  So much polluted groundwater was pumped out (over 800,000 gallons), in fact, that sinkholes began to form in the dry soil. It is like rinsing and squeezing a sponge, Little told me. The same flood that partially destroyed the old IBM country club helped to flush out some of the contaminants from the ground. Still, the chemicals bind to the dirt, and it seems unlikely that they will ever be totally eliminated.  Beyond the borders of Endicott, there remain seventy-six microchip manufacturing facilities in the United States. There are many more around the world, from South Korea to Taiwan, Germany to Singapore. Toxic TCE is not just a problem for IBM neighborhoods, then, but for computer manufacturers all over. Of the Superfund site National Priorities list, TCE is in 1,045 of 1,699.  Around the same time that IBM pollution came to light in the United States, manufacturing was being shipped overseas, along with the pollution. Even as dangerous chemicals like TCE are replaced, or in rare cases outlawed, the sheer demand for the product is perhaps the biggest danger. Production is valued over safety and product is prized over resources. Until this equation changes, Endicott will have many sister cities.  Life Goes On It is 7 a.m. in Endicott, the time the morning shift at IBM would start if the factory was still running. The light outside is a familiar arctic blue which makes the trees and snow seem both flat and harsh. I’m hungover, sitting on a bed in the Endwell Motel.  The Endwell Motel. Photo courtesy of the author.

Response: I spent last night at a bar called Close Quarters outside the reaches of the plume. My friend Sarah came here with me to take photographs, and she came to the bar too. Sarah drank mini bottles of white wine while I drank Labatt Blues. We shared a basket of tater tots, talked, and watched football on TV.


Prompt: We met an Italian guy, “Tim,” from here (townies call it “End-y-cott”) with a triangular nose and a tattoo of a big cumulous cloud spilling over his right hand. He bought me a shot of Jameson and I told him about my research. He said he didn’t work at IBM but he knew people who did, real old timers. He’s too young to have worked at this IBM. Instead, he drives an 18-wheeler for CVS.  Tim used to be a drug addict like a lot of people I meet around here. They are frank about it: sick but better now. It’s not so much opioids that are the problem these days but meth. It seems the desire is not in wanting to slow down, but instead to speed things up. The town population has dipped to 13,000, smaller than the IBM workforce at its height. Instead of manufacturing, people now work in retail, healthcare, or the service industry. Nearly 20 percent of the town lives in poverty.  Tim showed me videos of his fish tank on his phone. I kept asking, “Is that a goldfish?” “No,” he’d say. They all had different species names, bright yellows and oranges swimming across the slick expanse of his phone. He showed me his truck delivery route for the next day. He’d be driving north to Vermont, right by the factory where I used to work.  We began to talk of violence. He likes to fight, he said, smiling, “but when I see a flower, I see a woman.” That’s why men need women, he told me and Sarah. We traded stories about killing pigs. Tim’s story was about a butcher crying and shooting a pig that wouldn’t die. He cried so much he was blinded by his tears. He cried so much he started praying. Sarah and I told him about the pig we shot that ran away into the woods, how it had to be tackled and shot again. The instinct in nature — a flower, a pig, a town — that does not want to die was there both times. We talk like this about life and death as a way to talk about the poison of the plume, and our hope for the future.

Response: On September 26, 1991, surrounded by the cameras of the world media, eight people dressed in bright red jumpsuits sealed themselves inside a three-acre steel-and-glass dome in the Arizona desert filled with over three thousand species of animals and plants. They planned to remain inside for two full years, aiming to show that the structure — known as Biosphere 2 — was capable of sustaining life while completely sealed off from Biosphere 1, also known as Earth.  Amidst Biosphere 2’s seven biomes — desert, rainforest, savannah, marsh, ocean, city, farm — the Biospherians would grow their own food and conduct research on the workings of the closed system. They would rely on the plants and animals they lived alongside to produce oxygen, absorb carbon dioxide, fertilize the soil, and consume waste. Lessons from the experiment were expected to advance the prospects of human life in space and on other planets.  Biosphere 2 may seem to be little more than a bizarre episode in the annals of extravagant scientific undertakings. But we should take its history seriously as we think about the future of life on Biosphere 1, which today appears fairly dire.  In the summer of 2019, Greenland’s ice sheet lost nearly 200 billion tons of ice — three times the regular amount — while peat fires blazed across the Arctic. Two hundred reindeer starved to death in Norway while nearly two hundred gray whales have washed up dead on the western shores of North America since January. And that’s just in the past year in one part of the world. The UN warns that a million more species are threatened with extinction in the next few decades as a result not only of climate change but overfishing, deforestation, and unsustainable agricultural patterns.  You can understand why someone might want to build another world.


Prompt: Everything You Can Do, I Can Do Better These incidents suggest that the ecological systems we usually take for granted — sometimes referred to as Earth’s “life-support systems” — are starting to break down. Healthy ecosystems are generally self-renewing. They operate without humans having to do much. They are, in a sense, already automated, at least from our perspective. But climate change and other ecological pressures are interrupting their normal function.  Climate change has caused more rain in the Arctic, which then freezes into ice, making it hard for reindeer and herbivores to find food. Melting Arctic sea ice, meanwhile, may have reduced the amount of algae in Arctic seas, which feeds the amphipods that feed the whales. The prospect of further disruption raises a question: Is there something we could do to fill in the gaps? Is there a substitute for the work of nature — the work on which all other work depends? The fear that we’re running down nature’s abundance has a long history, of course. For economics — which is, after all, the study of scarcity — it’s nothing to worry about. Conventional economic theory holds that natural resource scarcity can be solved through substitution. When resources become scarce, they become more expensive, which leads people to use them more efficiently or to use other, more plentiful materials in their stead.  As the growth economist Robert Solow once quipped, “The world has been exhausting its exhaustible resources since the first cave-man chipped a flint.” In the eighteenth century, Thomas Malthus had worried about the ability to produce enough food; in the nineteenth, William Jevons had worried that the world would run out of coal. But new techniques, technologies, and resources overcame existing limits: petroleum replaced whale oil; steam engines replaced horses.  In the twentieth century, our capacity to create substitutes grew immensely. Many synthetic products were invented to take the place of natural ones. Declining soil nutrients could be replaced with artificial fertilizer; aluminum could replace copper; plastic could replace just about everything — wood, stone, metal, glass. Nuclear power appeared poised to offer cheap, near-limitless energy supplies in place of fossil fuels extracted from the earth.

Response: These advances gave rise to a way of thinking that we might call “substitution optimism”: the belief that humans can find substitutes for anything that nature does. But substitution optimists tend to neglect two problems. First, the development of substitutes assumes that the price of scarce goods will rise. What about scarce goods that don’t have a price? In particular, what about the services freely provided by nature? The services of atmospheric cycles and pollution-absorbing forests cost nothing — which mean that as they grow scarcer they do not get more expensive, and do not spur the development of technological replacements. Today, those resources — what we might think of as the earth’s reproductive rather than productive functions — are the ones most under threat. Like human reproductive work, they operate in the background of economic production, providing the basic functions necessary for life.