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

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


Prompt: Bright Orange to Blood Red The Unicode notation for the Hindu temple emoji is U+1F6D5. In the Twitter rendering of the emoji, the graphic has a stepped structure, tapering upward in tones of saffron to a tall spire, with a prominent two-fanged flag at the top. Hindu temple emoji. Source: Iconify (CC BY 4.0).

Response: Its architectural form is an ambiguous mix of the two most canonized styles of Indian temple architecture, the north Indian Nagara and south Indian Dravidian. In this way, it seems to combine two major streams of ancient temple architecture, each with many offshoots and tributaries, into a single statement. It also shares many features of the temple that is to be built on the contested site in Ayodhya, according to a design that has recently been circulated on social media by the Modi government’s Shri Ram Janmbhoomi Teerth Kshetra, a trust constituted to lead the construction and management of the temple.


Prompt: The emoji was originally designed by Girish Dalvi, a professor at the Industrial Design Centre of the Indian Institute of Technology, and Mayank Chaturvedi, who at the time was working for an agency of the Maharashtra state government that is dedicated to promoting Marathi language and culture, including through Unicode representation. A first version of the proposal was sent back by the Emoji Subcommittee for clarifications about whether the proposed image would mean “Hindu temple” to anyone who saw it, and whether it would be the best symbol to account for regional differences in what temples look like. In response, Dalvi and Chaturvedi argued that their design used a “common architectural grammar” among Hindu temples, and that the saffron color provided “semantic reinforcement” because the “saffron colour is often associated with Hinduism.” They also claimed that, like their design, “most Hindu temples have a flag at the apex.” This rationale used Unicode’s criteria to justify the inclusion of the emoji’s most blatantly Hindu-supremacist symbols. In reality, there’s an extraordinary diversity to Hindu temple architecture. Many temples are whitewashed, while others are brilliantly polychromatic. In recent years, though, the color saffron—in hues from bright orange to blood red—has become increasingly associated with Hindu nationalism. There’s even a word, “saffronization,” for the Hindu right’s attempts to rewrite everything from daily news to the history of the country in a way that reinforces Hindu supremacism: rechristening cities and streets that had Islamic-sounding names, purging textbooks of accounts of Mughal rule and heritage, claiming that ancient Hindus had “airplanes, stem cell technology, and the internet,” and generally presenting a glorified version of the past in which Hindu civilization is always virtuous and always supreme. Online, saffron is used in a range of ways by members of the Hindu right to signal their allegiance to the idea of a Hindu-first nation, and to issue threats to non-Hindus—for example, by saturating images with saffron tones, or by using saffron as the background color for aggressive text memes.

Response: The red swallow-tailed pennant that flies from the top of the temple emoji is also quintessentially Hindu-supremacist. It isn’t necessarily the case that most temples have flags, and the temples that do have flags rarely have a flag of this kind. The pennant in the emoji, however, looks exactly like the bhagwa dhwaj, the banner of the RSS. Although it doesn’t fly over many temples, it is sometimes used by Hindu fundamentalists to distinguish Hindu shops and other buildings from those owned by non-Hindus—information that could be put to vicious use during a pogrom, and which has come to inspire fear in non-Hindus in daily life.


Prompt: The Unicode Consortium’s criteria for new emojis require that the designs should be at once unique and representative, so that, for example, the beer mug emoji 🍺 is instantly recognizable as a mug of beer while at the same time standing for beer in general. Part of what this means is that there will likely never be another Hindu temple emoji that could exist alongside, and contest the symbolism of, this one. The major social media platforms, software companies, and hardware manufacturers all adapt Unicode’s approved emoji designs to their own products. Some of their versions of the temple are more clearly Hindu-supremacist than others, but all share at least one feature in common with the emoji’s original design: many have pennants, and most are saffron. The upshot is that the Unicode Consortium’s decision has massive downstream effects: whenever anyone adds a temple emoji to their Facebook post, they’re reproducing a symbol of Hindu supremacism. (Members of the Unicode Consortium’s Emoji Subcommittee did not make themselves available for comment before this piece went to press.) One of the emoji’s designers, Dalvi, told me that his design has been misinterpreted because of the political context into which it was released. “The timing could not have been more unfortunate,” Dalvi said. “As a designer, this is the pitfall. My design can be politicized and used towards ends that I cannot control.” But it’s impossible to deny the Hindu-supremacist symbolism of the emoji, especially given that countless other approaches were theoretically possible. The consortium wanted to make sure the emoji was clearly a “Hindu temple,” but they didn’t say how to accomplish this. If Dalvi didn’t intend for his emoji to be a badge of Hindu supremacism, his design choices show just how successful the RSS has been at promulgating its vision for India.

Response: Permanent Supremacy Use of the :hindu_temple: emoji (as its shortcode is known on Slack, GitHub, and other platforms) is still picking up, spurred on by software that suggests it whenever users type “temple,” “mandir,” or other words on their keyboards. The emoji has been adopted by some social media accounts dedicated to sharing temple photos and discussing temple tourism, but it has also quickly become a shorthand for expressing support for Hindu supremacy—or issuing direct threats—when included in social media usernames and messages.  The emoji has something in common with other ways in which Hindu supremacists have deployed language online to further their cause. Like the emoji, certain phrases purport to be descriptive but actually promote dangerous narratives about who is an enemy and who is a supporter of Hinduism and India. These phrases include “Love Jihad,” which refers to a supposed phenomenon of Muslim men seducing Hindu girls, and “Urban Naxal,” which is used to describe a certain type of English-speaking, urban-dwelling leftist thought to be sympathetic to Maoist revolutionaries operating in parts of the Indian countryside.  The central danger of the emoji is that it is as generative of people’s ideas about what counts as Hindu or Indian as it is reductive of Hinduism’s and India’s complexities. “We have to be very careful with the process, that what we ultimately encode is going to be something that has permanence and will stand the test of time,” Craig Cummings, a senior technical product manager at Amazon and the vice chair of the Unicode committee that oversees the approval of new emojis, says in Picture Character, a 2019 documentary about emojis. What the consortium has helped give permanence to in this case is the Rashtriya Swayamsevak Sangh’s vision for Hindu supremacism in India and worldwide. It is a small but potent example of the way that Silicon Valley has chosen to make money from hate.


Prompt: Pit Schultz was sitting in a Kreuzberg art gallery, less than a mile from the ruins of the Berlin Wall, when he sent one of the first emails to a mailing list that he had just helped launch. Schultz laid out a vision for what it might become: It should be a temporary experiment to continue the process of a collective construction of a sound and rhythm—the songlines—of something we are hardly working on, to inform each other about ongoing or future events, local activities, certain commentaries, distributing and filtering textes, manifestos, hotlists, bits and blitzmails related to cultural politics on the net. It’s also an experiment in collaborative writing and developing strategies of group work… The list is not moderated. Take care.

Response: It was June 1995, and the internet was changing in fundamental ways. The US government–funded National Science Foundation Network (NSFNET), once the backbone of the internet, had been decommissioned a few months earlier. New companies like Amazon, Yahoo!, and Netscape were racing to cash in on an early wave of commercialization, and a rising priesthood of techno-utopians gathered around Wired magazine—launched in San Francisco in 1993—to herald the coming digital economy as the harbinger of a more unified, democratic, and horizontal world.


Prompt: The 1990s were a heady time, but not everyone was convinced. If Wired was the pulpit for a new gospel of venture-funded tech, Schultz’s mailing list, called Nettime, was an effort to build a home for the early commercial internet’s discontents. Drawing on various contemporary anti-capitalist currents, from the anti-globalization movement to Italian autonomism to Berlin’s lively squatter movement, Nettime aimed to synthesize an alternative to the techno-determinist optimism oozing out of Silicon Valley: the worldview that media theorist and Nettime regular Richard Barbrook named the “Californian Ideology” in a 1995 essay co-written with Andy Cameron.

Response: The Californian Ideology, through its “bizarre mish-mash of hippie anarchism and economic liberalism,” celebrated the rampant commodification of digital networks as a force for personal liberation. The 1990s are often remembered as a time in which this vision of the internet went unchallenged, but the Nettime crowd wanted to chart a different path forward. They developed theories of digital culture, pioneered tactics for new media activism, and wrote ground-zero critiques of the commercial internet as it took shape around them. The mailing list itself was also a platform for experimental forms of collaborative writing that tried to embody a different experience of being online. At a moment in history when profiteers and privatizers were terraforming the internet into the market-saturated system we know today, the Nettime circle gestured toward a more collective, less commodified alternative—but only vaguely.


Prompt: A Collective Undertaking to Deconstruct the Utopian Wired Agenda Nettime lived on the internet, but it came from Europe. The idea for the mailing list was first proposed by Schultz and his friend Geert Lovink at a meeting of the Medien Zentral Kommittee—German for “Central Media Committee”—at the Venice Biennale. The Kommittee was a loose collective of academics, net artists, and new media activists who believed in the necessity of creating alternatives to both the Silicon Valley worldview and the commercial internet it had inspired. In doing so, they drew on materials closer to home, building an umbrella for an anarchic strain of critical net culture that flourished across Europe.

Response: Whereas Wired drew from the ranks of a thriving private tech sector, Nettime’s milieu came from a relatively communal and localized hacker culture. This took shape, for example, around spaces like c-base, the member-funded nucleus of Germany’s rapidly expanding hackerspace scene, which went on to promote free public internet access via wireless networks. Another spiritual progenitor was Austria’s Public Netbase, a nonprofit internet service provider and new media initiative that promoted “network democracy from below,” openly clashing with the country’s right-wing government.


Prompt: Europe had also been home to a number of ambitious publicly funded efforts to extend social and civic life into the digital realm. Perhaps the most famous was France’s publicly owned Minitel network. Rolled out in the 1980s, Minitel was the most successful online service prior to the modern internet. It ran across nationalized phone lines, distributed free terminals, and boasted a peak of twenty-five million users out of a total population of sixty million.

Response: By the 1990s, similar initiatives had cropped up around Europe at the municipal level, like Amsterdam’s Digital City (DDS). A citywide free-net, DDS grew out of the pirate radio scene and aimed to create a universally accessible network that could guarantee certain basic rights online. There were public terminals, and anyone could sign up for a free account with email, internet access, and space for a homepage. Instead of being organized around the free market, the network’s architecture was designed with the city metaphor in mind: you received mail at the “post office,” links were accessed through a “station,” “public squares” hosted government services, organizations and companies could rent “shops,” and the entire system was navigable through a graphical city interface.


Prompt: Public networks like Minitel and DDS can help us understand why European hacker culture diverged so sharply from the cult of Silicon Valley. While the US obscured the internet’s publicly funded origins behind a veil of bootstraps entrepreneurialism, many Europeans first encountered mass computer networks as explicitly public entities.

Response: Nettime took inspiration from these projects, and attracted many of their architects, but the list itself represented a different kind of intervention. If projects like c-base, Public Netbase, and DDS explored new ways of creating and organizing networks, Nettime was also an attempt to theorize and embody a new way of experiencing them. In the words of Geert Lovink—a cofounder of both Nettime and Amsterdam’s DDS—the list was “a collective undertaking to deconstruct the utopian Wired agenda. Not directly, in word or academic texts, but by doing.” In the early to mid-1990s, the internet could be a disorienting place; a seemingly endless labyrinth of what Baffler contributor Kate Wagner described as “haphazardly designed, amateur-generated sites.” But it also often felt more grounded and neighborly than today’s internet. Most online communities were small and focused enough for participants to develop personal familiarities, shared norms, and something like a localized collective consciousness—“netiquette,” as it was called. In a time before platform capitalists had carved the net into siloed empires of attention-time, it was still possible for anti-capitalists of the sort that gathered around Nettime to see the internet as a catalyst to dissolve commercialization and competitive individualism—but by 1995, things were starting to change. Developments like online shopping, powerful search engines, and interactive advertisements were beginning to rend the federated collectivism of the early internet into a world of quantitative efficiency and algorithmically mediated “users.” A window of possibility seemed to be closing. Nettime was determined to keep it open.


Prompt: Living in Social Time Nettime’s name was chosen as an alternative to “cyberspace,” the dominant metaphor for understanding the internet in the ’90s. “Cyberspace” renders the internet in spatial terms, and evokes images of highways, libraries, webs, clouds, and shopping malls. All of these tend to naturalize concepts like scarcity and enclosure, which in turn lend themselves to the possibilities of exclusive ownership, exploitation, debt, or rent.

Response: By contrast, “nettime” renders the internet temporally. Whereas the concept of a spatial network frames humans as occupants of a fixed virtual world—one that could be chopped up into shopping malls—“nettime” suggests that their mutual engagement fundamentally constitutes the network itself—that there is no network without the nodes it connects. Rather than passively “going” online and browsing shelves, we actively produce the network together, in real time, through our collective participation. “The time of nettime is a social time,” wrote Pit Schultz in the introduction to an October 1996 Nettime publication. “Time on the net consists of different speeds, computers, humans, software, and bandwidth, the only way to see a continuity of time on the net is to see it as an asynchronous network of synchronized time zones.” In trying to embody this “social time,” Nettime pioneered a practice of “collaborative text filtering,” a continual, self-organizing process whereby texts were submitted to the broader group, replied to, expanded upon, and ultimately flowed into a collective train of thought. The original vision was to initiate something like a perpetual conversation, without editors, boards, gatekeepers, or centralized moderation; the list’s “filter” was the collective interests and capacities of its self-selecting membership. In the introduction to ReadMe!, a collectively edited book of Nettime essays published by Autonomedia in 1999, the list is described as “always different from what it was a moment ago; it’s always discovering something new about itself. As such, it is a working implementation of what subjectivity might become in an online environment.” In practical terms, this meant that Nettime served numerous functions: it was a tactical bulletin for Europe’s anarchic hacker community; an open source prepublication platform for academics; and a forum to discuss current events, announce events, post manifestos, and theorize the commercial internet as it came into focus. Prominent works to come out of Nettime were occasionally circulated at conferences, republished in magazines, or rounded up into physical publications—but online, the emphasis was always on a perpetual process of becoming. Whereas early online messaging programs like AIM (AOL Instant Messenger) and early virtual communities like The WELL (Whole Earth ’Lectronic Link) operated on the concept of exchanging content between discrete, static users, hierarchically sorting information, and publishing text in a self-contained, finalized state, an unmoderated mailing list leaves participants with no common structure or interface but one another.


Prompt: Collaborative text filtering was meant to recover, in a many-to-many communication system, the experience of temporally interconnected thought that is foundational to real-time conversation. Nettime members developed a collective style of writing that embraced its own incompleteness, questioning the notion that any text can or should be understood as finished, or that any thought is worth isolating from its discursive context. This practice generated prescient missives and essays on applying copyleft practices to non-software intellectual property, the reality that the internet was already doing more to restructure labor rather than expand leisure, the creeping power of data collection and surveillance, and the creation of a new class of “information proletariat.” Above all, it emphasized the network’s participants as its creators, rather than mere residents.

Response: Haunted Variables Nettime fought for alternatives to the privatized net, pushed back against the Californian Ideology, and experimented with a collective experience of being online. But the list had considerably less success in articulating a comprehensive alternative to the commercial internet at scale, or bringing its experience to the broader public. The mailing list grew to thousands of subscribers, but its vision for an alternative to the commercial internet remained dormant, and its members ultimately failed to alter the course of the internet’s commodification.


Prompt: This failure was partially rooted in a vagueness at the heart of Nettime’s challenge to the Wired line. While mailing list participants could momentarily enact a more collective experience through collaborative text filtering, the lack of organization also ensured that no consensus around a comprehensive alternative would emerge. Nettime participants tended to share certain anti-capitalist principles—a preference for publicly owned internet infrastructure, the decommodification of intellectual property, and the abolition of various digital hierarchies—but the particulars were subject to endless debate. Real-world implementation was thus limited to aesthetic interventions—culture jamming, détournement, and critical net art—and local, uncoordinated, long since defunct projects. Media theorist McKenzie Wark later observed that Nettime was united by “a negative consensus around the need for a countervailing theory.” That negative consensus never evolved into a positive one, because a single countervailing theory never emerged.

Response: Attempts to build such a unified theory tended to fall flat, as when Richard Barbrook sent an essay called “Cyber-Communism” to the list in 1999. “A spectre is haunting the Net,” it began, “the spectre of communism.” Barbrook’s central contention was that everyday internet users were already on a path to transcending the profit-driven logics of the privatized net, thanks to the popularity of gift economies around phenomena like user-generated content, open source software, and peer-to-peer networking. In other words, he argued that the experiments in collaborative text filtering that Nettime was undertaking to cultivate a more collective experience of the internet were already being superseded by mainstream initiatives with far greater reach.


Prompt: Twenty years later, a commercial internet dominated by a handful of platform oligarchs makes Barbrook’s optimism easy to dismiss. But it didn’t take the power of hindsight to notice that his “wait and see” determinism was functionally indistinguishable from the techno-utopian Wired set. A satirical Nettime response to Barbrook’s essay captured the point: Subject: THE GIF ECONOMY: How Several Layers of Lossy Images Are Synthesized into a Moving Image that Will Animate the Masses and Inspire Them to Do What They're Doing Anyway, Namely, Clicking Their Way to Liberation; Or, How I Learned To Stop Worrying and Love the Californian Ideology<-----! DECLARE ALL VARIABLES !----->HAUNTED_VARIABLE = ( EUROPE | WORLD | NET)<-----! END DECLARE ALL VARIABLES !----->Just imagine … a specter is haunting HAUNTED_VARIABLE for the second time. Luckily, if it’s a tragedy the first time, the second time around it’s only a farce — so fear not: no more messy dogmatic truths, state bureaucracies, apparatuses of oppression, proxy wars, or national collapses. CyberCommunism[TM] GUARANTEES you won’t have to change a single setting, preference, or property in order to build a communist society! You can contribute DIRECTLY to the construction of a workers’ paradise on a GLOBAL scale from the comfort and privacy of your own home or office WHENEVER you feel like it - just by surfing the Internet! Largely derided on Nettime, “Cyber-Communism” nonetheless displayed a foundational shortcoming of critical ’90s net culture: an aversion to the firm commitments and big-picture thinking that would have been essential for mounting a serious challenge to the internet’s rapid privatization. One of Nettime’s signature critiques of the Wired line was that the techno-utopian gospel’s apparent optimism about the networked future disguised a fundamental pessimism about the role that humanity might play in it. Humanity was not the subject of this future; technology, mediated by the profit motive, was. Utopia was coming, whether we liked it or not.

Response: Yet Nettime’s participants often reiterated this same idea in a slightly different register. In the end, Barbrook shared the techno-utopian faith that, through the commercial internet, humanity was automatically generating a better future, albeit a communist rather than a capitalist one. Lacking a coherent political program, such immanentist arguments amounted to little more than a vague hope that things would work out in the end or, at best, a collection of abstract demands: “Deprivatize corporate content, liberate the virtual enclosures, and storm the content castles!” Echoes of Electric Agora By 2020, a handful of Nettime’s once-utopian ideas have become ubiquitous facts of the commercial web—but only in forms that have been disfigured to the point of unrecognizability. Collaborative text filtering, for example, has become a core feature of how modern social media platforms create and manage feeds. Rather than employing formal gatekeepers or editorial staff, websites like Facebook, Twitter, and Google rely on collective behavior and decentralized editorial discretion to produce evolving, personalized hierarchies. But most of the agency has been transferred from conscious human actors to algorithms, with networks operating beyond the scale of comprehension.


Prompt: Meanwhile, Nettime was forced to abandon its anarchic commitment to total decentralization around the turn of the millennium, and began tolerating light moderation once the list got big enough to encounter a few deliberate disruptors—as well as a general tendency for men, in particular, to use it “to compare the length of their bookshelves,” as communications professor Matthew Fuller put it in 1998. Given that decentralized collaborative text filtering proved unmanageable at scale, and given the extent to which corporate social media platforms have adapted the same techniques to alienate “users” from their own experiences of the internet, it may be best to understand Nettime’s experiments in collective subjectivity as windows into a time when the internet’s corporate trajectory seemed less inevitable, rather than practical steps toward building a decommodified internet from within. Like the broader anti-globalization movement that influenced it, the Nettime circle was ultimately naïve about how easily the system would reabsorb aesthetic transgressions and hyperlocalized struggles.

Response: Nettime has receded into obscurity since its halcyon years of 1995–2001, but the list is still running and open to new members after twenty-five years. Some of Nettime’s early disciples have even risen to positions of real influence, buoyed by the post-2016 techlash and the resurgent popularity of democratic socialism. Richard Barbrook, for example, coordinated Jeremy Corbyn’s 2016 “Digital Democracy Manifesto,” which proposed significant state support for platform cooperatives, open source software, and public broadband expansion—structural solutions that harken back to the pre-commercial European internet. As Nettime’s critiques bubble back to the surface amidst a renewed climate of tech skepticism, the list’s archive provides lessons, warnings, and a usable intellectual history for today’s tech-skeptical left.


Prompt: The Nettime circle may have failed to stall the rapid privatization of the internet or dismantle the Californian Ideology in the ’90s, but today, these forces face far more mainstream scrutiny. As an ascendant techlash resurfaces many of the European hacker culture’s early critiques of the private internet, and looks for tools to build an alternative, the list’s archive reveals a forgotten forerunner that kept a torch burning in dark times.

Response: Breonna Taylor, Ahmaud Arbery, and George Floyd were murdered in early 2020, victims of persistent anti-Black violence. In the midst of nationwide uprisings over their deaths, leaders in the technology industry responded. Amazon donated $10 million—roughly forty-five minutes worth of its gross annual profits—to racial justice organizations such as the National Association for the Advancement of Colored People. Social media companies like Facebook and streaming services like Netflix made content created by Black people more visible. Sundar Pichai, the CEO of Google and its parent company, Alphabet, promised a number of corporate commitments to racial equity, such as establishing anti-racist educational programs within the organization. And almost all of these actions were accompanied by pledges to bring more Black and brown people into tech company ranks—a desperately needed measure in a chronically white industry.


Prompt: When tech leaders made those pledges, they often presented themselves as breaking bravely with the past: they would take unprecedented steps to overcome the implicit bias within their own companies and the structural racism of the industry as a whole in order to forge a more equitable future. (“Google commits to translating the energy of this moment into lasting, meaningful change,” Pichai wrote in a letter to the company.) But there’s good reason to doubt this self-presentation. In 2014, following pressure from public figures including Reverend Jesse Jackson, Google, Facebook, Apple, and Microsoft publicly disclosed their diversity data. Only 6 percent of Apple’s workforce, 2 percent of Microsoft’s, and 1 percent of Google’s and Facebook’s identified as Black, according to statistics compiled by Wired. Each company vowed to do better; Apple’s Tim Cook said the company would become “as innovative in advancing diversity as we are in developing products.” By 2018 and 2019, however, the percentage of Black tech workers at Facebook, Google, and Microsoft had increased by only one point; Apple’s numbers hadn’t changed at all.

Response: Far from making a break with the past, when tech leaders pledge to diversify their companies, they are drawing from a playbook drafted over the course of the industry’s history. Information technology firms have been trying—and largely failing—to become more racially representative since at least the 1960s. To understand some of the reasons why the tech industry has failed to become more diverse year after year, decade after decade, it’s useful to go back to the earliest large-scale efforts by a major technology company—IBM—to diversify its workforce.  IBM has been actively trying to bring more Black and brown people into its workforce longer than any other major tech company, and it has adopted or invented the widest range of strategies to do so. If any company has had a margin of success in this, it’s been IBM, and all of the tech companies that have come after it have in some way followed its example. At the same time, IBM’s history is instructive because the company has been at the forefront of producing racist information technologies that have disparately harmed the very same people the company has spent decades trying to recruit—a dynamic that also characterizes many of today’s tech giants.


Prompt: IBM’s flawed motives, failed strategies, tempered successes, and massive contradictions over more than half a century provide critical lessons for today’s tech industry to learn from if it is serious about advancing racial justice and equity. What the history of IBM shows is that creating racial equity in tech requires a commitment from institutions beyond the industry. It also demands that we rethink the sorts of technology that we allow tech companies to build.

Response: The Original Bootcamp In 1964, US civil servants transformed a former army base known as Fort Rodman, on the outskirts of New Bedford, Massachusetts, into the campus for an audacious new experiment in technical education. The base would host hundreds of male high-school dropouts—most Black, some white and Latino, all poor or working-class—from across the country for a free fourteen-month training program designed to produce graduates who could go on to entry-level jobs at tech companies, including IBM. As IBM president Tom Watson Jr. later recalled in his memoirs, “The idea was to train 750 hard-core unemployed each year—black high school dropouts from the inner city who had never held jobs.” This was IBM’s first and, in many ways, most ambitious diversity initiative. It was run by IBM but funded by the federal government as part of the Job Corps, a free education and workforce training program that was conceived by the Kennedy administration and which later became a key part of President Lyndon Johnson’s Great Society, including the so-called War on Poverty. The Job Corps was one of the ways in which Johnson sought to quell the social and economic frustration fueling Black and working-class political mobilization during the civil rights era in order to maintain and expand Democratic power.  IBM had two major goals in launching the Fort Rodman experiment. First, it hoped to ingratiate itself with the federal government, a source of lucrative contracts for everything from tabulation machines for the US Census to computer consoles for operating NASA space flights. Second, and perhaps more critically, the computing giant needed to train a large entry-level technical labor force to help fuel the company’s rapid expansion. In 1965 alone, IBM acquired twenty-six new facilities, and in the subsequent five years it would double its annual revenue to roughly $7 billion ($45 billion in 2020 dollars).


Prompt: The first crop of 350 students arrived at Fort Rodman in January 1965. They hailed “from the big cities and the small ones, the shut-down mining towns and the farm country” in New York, Texas, Alabama, and thirty-one other states, according to a 1966 promotional film about the program. Some of these young men may have been lured to Fort Rodman by postcards that featured an aerial photograph of the base on a sunny day, looking almost like a beach resort, and on the other side the text: “A HANDUP—NOT A HAND OUT.” One student said that the program was his last resort; a judge told him it was either Fort Rodman, or else.

Response: Students at Fort Rodman were separated into small cohorts, with one instructor assigned to five or six students. The instructors were white college graduates, some from the Peace Corps, who had been trained on site to be “tutor-counselors” to the young men who for more than a year would make Fort Rodman their home. The tutor-counselors were expected to be mentors and to bond closely with the boys; they ate with the students, hung out in barrack-style dormitories where as many as fifty slept in bunk beds with military-cornered sheets, and played football together. This was as much a part of the students’ training as their remedial math and language courses and their regimen of office skills training, which included how to use typewriters, calculators, and keyhole-punch and data-processing machines.  In the 1966 promotional film for Fort Rodman, students seem impressed as an IBM employee shows them around a new punch-card tabulating machine. “How much time would this machine save compared to how you do ’em by hand?” a Black student in a shirt and blazer asks in the film. “Take a payroll application, for example,” the IBM instructor answers. “A payroll that might take an entire week to prepare could be done on this machine in, say, two to three hours at the most.” But if the electronic magic showcased by IBM captivated the boys at Fort Rodman, it wasn’t enough to help them develop proficiency in the skills needed to get a technical job at a company like IBM. Most of what we know about the program comes from promotional materials that reflect how the people running the program, and IBM leadership, idealistically imagined it working. Even in these sources, however, the causes of Fort Rodman’s failures are clear. Some were operational: for example, despite being designed to provide small-group, individualized attention, the program hired too few staff to meet student needs. Reports noted that students were often neglected by their instructors. Some students stopped showing up for class.


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

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


Prompt: IBM doesn’t seem to have tracked its diversity programs with any rigor, making it difficult to know just how many they ran, where, and to what effect. But between 1978 and 1981, the period when IBM was most public about the success of its diversity programs, roughly 20 percent of IBM's new hires were non-white, and the number of non-white managers in the company increased from 1,973 to 2,600.

Response: But like Fort Rodman, IBM’s other diversity programs were flawed in important ways. Most notably, they focused on short-term, skills-based training for people whose educational background made it unlikely they would move beyond low-level positions at the company. Frank Cary, the chairman and CEO of IBM throughout most of the 1970s, admitted as much in a speech to the company’s board and stockholders in 1974. “We’ve made good progress on one of our objectives—bringing into IBM capable and highly motivated minorities and women,” Cary said. “Our second objective is taking longer to achieve: helping minorities and women qualify themselves for advancement at every level of the business consistent with their abilities and their growing population in the company.” Among the obstacles to promoting talented women and people of color, Cary’s comments implied, was the desire among members of IBM’s managerial class to hold onto the privileges conferred by their whiteness. “The relevant question I’m asked most frequently by IBM managers,” Cary said, “is: ‘How can we do that without practicing reverse discrimination?’” This attitude was connected to a more fundamental problem. As much as IBM did in this period to try to remove the metaphorical “Whites Only” sign from its company doors, racism at the company wasn’t just a cultural or structural issue—it was part of its long-term business model. As early as the 1920s, IBM marshalled its computing powers to support eugenics, sterilization, and population control in Jamaica. The company sold technology to Hitler’s regime that allowed the Nazis to tabulate census figures in order to identify and eventually murder Jews, and it sold similar technologies to South Africa to run the apartheid state.


Prompt: From about 1961 through the late 1960s, IBM was also deeply invested in helping federal, state, and local governments imagine, develop, and deploy carceral technologies that became known as “criminal justice information systems.” IBM engineers, designers, and salesmen aggressively marketed computer hardware and software applications to the law enforcement community. Through lucrative contracts with big city police forces like the NYPD, research and development partnerships through President Lyndon Johnson’s 1965 Crime Commission, and millions of dollars in grants from the newly formed federal Law Enforcement Assistance Administration, IBM laid the foundations on which today’s policing and surveillance infrastructure has been progressively built over the past fifty years.  In 1968, for example, IBM debuted a system called ALERT II in Kansas City, Missouri. The system began as a database—a place to store police records about arrests, adjudications, jailings, and juvenile justice cases. But by the early 1970s, when it was fully built out, ALERT II, along with similar systems across the United States, was a nationally networked platform that provided law enforcement the ability to profile, surveil, target, and deploy police manpower based on the racial composition of neighborhoods and locations where crime allegedly predominated.  This reinforced a vicious cycle of racist policing. Because police believed Black people committed more crime, they deployed more police to Black neighborhoods. That led to more arrests, which meant Black people were captured more in police databases. Relying on that data to determine where to target police resources meant policing Black neighborhoods more intensively, thus perpetuating the cycle. As a result, entire communities were effectively criminalized in part by the technologies IBM was building.

Response: Over the next three decades, through the 1970s, 1980s, and 1990s, such criminal justice information systems—some built by other companies following IBM’s lead, many built by IBM itself—proliferated throughout the US, criminalizing Black and brown communities across the country. Since then, IBM has developed newer technologies with even more expansive law enforcement applications, including facial recognition, predictive policing, and police management systems—all of which wreak havoc on Black and brown people in similar ways to IBM’s earlier generation of carceral technologies.


Prompt: It’s impossible to draw clear lines of causality between the racial makeup of IBM and the racist carceral technologies it has built. Would an organization with more Black and brown people in roles with seniority and power necessarily eschew helping law enforcement agencies criminalize communities of color? Would an organization that didn’t build racist carceral technologies have more Black and brown people eager to join its senior ranks? Or would class interests trump racial solidarity so that even an IBM that was more diverse at all levels would still choose profit over racial justice?  Those may be unanswerable questions, but there is nevertheless a clear thread connecting IBM’s diversity projects with the racist technologies it developed. In both cases, IBM saw Black and brown people as easily exploitable sources of profit—either in the form of low-wage labor, or as the material inputs that fed its policing technologies.

Response: Building Black Tech In the half century since the Fort Rodman experiment ended, big tech companies have launched many other diversity programs. But the numbers of Black and brown people in those companies, and the underlying logics of racialized capitalism that powers the technology industry, have remained largely unchanged. IBM’s supply-side labor programs continue in the form of legions of coding bootcamps that promise Black and brown young people entree into the tech industry—though, in the absence of government and philanthropic support, these are run almost entirely as for-profit ventures.  The same approaches have correlated with the same results. The percentage of full-time Black employees in the tech industry today is about the same as IBM’s was in 1965—roughly 2.5 percent. This lack of progress is reflected in, and may in part be caused by, the attitudes of the people who run these companies: a new report, People of Color in Tech, reveals that the majority of tech founders and CEOs believe that diversity work is ineffective. Roughly half of that same group are unconcerned about the fact that only 1 percent of tech entrepreneurs funded by VCs are Black. This sort of indifference is echoed in the experience of Black tech workers, who (more than their white peers) say they have trouble finding mentors at the companies they work for.


Prompt: What would a more effective approach to improving the diversity of the tech industry look like? Three lessons stand out from the history of IBM’s diversity programs. First, we need to ditch the supply-side approach that only prepares people for the lowest-level jobs, with the goal of creating an expendable and increasingly cheap labor force. Second, we can’t leave it to the tech industry to change itself—we need government watchdog agencies like the Equal Employment Opportunity Commission to hold companies accountable, and the commitment of public resources, like those marshaled by Johnson’s Great Society programs, to help transform society itself. Finally, we can’t just seek to change racist cultures or structures at tech companies—we need to fundamentally change their business models.  In June 2020, IBM’s recently appointed president, Arvind Krishna, announced to Congress that the company would no longer sell or develop facial recognition technology. He did so out of an explicit concern for racial justice, recognizing that these technologies have been and continue to be used to devastate people of color, including those within his own company. The tech industry should follow IBM’s lead in examining its products, investments, and research and development projects. When these are inconsistent with racial equity and justice, the companies must abandon them. Diversity in tech is not just about sharing the gains of technology. It’s about reimagining the tech we build and why.

Response: What would a socialist economy look like? The answers to this question vary, but most of them involve planning. A capitalist economy is organized through the interaction of prices and markets. A socialist economy, by contrast, would be “consciously regulated… in accordance with a settled plan,” to borrow a line from Marx. But how would such a plan be made and implemented? This has been a matter of sharp debate among socialists for more than a century.  One camp has placed particular emphasis on computers. These “digital socialists” see computers as the key to running a planned economy. Their focus is on algorithms: they want to design software that can take in information on consumer preferences and industrial production capacities—like a gigantic sieve feeding into a data grinder—and output the optimal allocations of resources.


Prompt: Over the years, there have been a number of experiments along these lines. In the 1960s, the Soviet mathematician Victor Glushkov proposed a nationwide computer network to help planners allocate resources. With the help of the English cybernetician Stafford Beer, Salvador Allende’s administration in Chile tried something similar in  the 1970s, called Cybersyn. Neither project got very far. Glushkov’s idea ran into resistance from the Soviet leadership, while Pinochet’s coup ended Cybersyn before it was fully implemented. However, the dream lives on.

Response: Today, digital socialism could obviously do much more. The internet would make it possible to funnel large quantities of information from all over the world into planning systems, almost instantaneously. Gigantic leaps in computer power would make it possible to process all of this data rapidly. Meanwhile, machine learning and other forms of artificial intelligence could sift through it, to discover emergent patterns and adjust resource allocations appropriately. In The People’s Republic of Walmart, Leigh Phillips and Michal Rozworski argue that large companies like Walmart and Amazon already use these digital tools for internal planning—and that they now need only be adapted for socialist use.


Prompt: While there are certainly emancipatory potentials here, they are far from adequate to the task of planning production in a post-capitalist world. The digital socialist focus on algorithms presents a serious problem. It risks constraining the decision-making processes of a future socialist society to focus narrowly on optimization: producing as much as possible using the fewest resources. To travel down this road is to ignore and discard vast amounts of qualitative information, which remains crucial to achieving many of the ends and goals of a socialist society.  After all, the societies of the future will want to do more than just produce as much as possible using the fewest resources. They will have other goals, which are more difficult to quantify, such as wanting to address issues of justice, fairness, work quality, and sustainability—and these are not just matters of optimization. This means that, no matter how powerful the planning algorithm, there will remain an irreducibly political dimension to planning decisions—for which the algorithm’s calculations, no matter how clever, can only serve as a poor substitute. Algorithms are essential for any socialist planning project because they can help clarify the options among which we can choose. But human beings, not computers, must ultimately be the ones to make these choices. And they must make them together, according to agreed-upon procedures.

Response: This is where planning protocols come in. They streamline decision-making by clarifying the rules by which decisions are made. Deployed in concert with algorithms, protocols enable a range of considerations—besides those available to an optimization program—to enter into the planning process. We might say there is a division of labor between algorithms and protocols: the former discard irrelevant or duplicate options, clarifying the decisions to be made via the latter.  Putting both algorithms and protocols to work, people can plan production with computers in ways that allow their practical knowledge, as well as their values, ends, and aims, to become integral to production decisions. The result is something that neither capitalism nor Soviet socialism allowed: a truly human mode of production.  The Price Is Right Any serious attempt at socialist planning has to reckon with the problems posed by the “socialist calculation debate,” a decades-long argument that has influenced how generations of socialists have imagined a post-capitalist future. The right-wing Austrian economist Ludwig von Mises kicked off the debate in 1920 with “Economic Calculation in the Socialist Commonwealth,” a full-frontal assault on the feasibility of socialist planning.  At the time, this wasn’t just a theoretical question. The revolution was already well underway, not only in Russia, but also in Germany, and very nearly in Italy and other countries. Socialists claimed that, with the capitalists cast aside, they could use modern machinery to construct a new type of society, one oriented around human needs, rather than profit. Everybody would get access to the goods and services they needed, while working less.  Mises argued that socialists were wrong on both counts. Instead, people in a socialist society would work more hours and get less for it. That’s because, in his view, the efficiency of modern economies was inextricably connected to their organization via the market, with its associated institutions of money and private property. Get rid of these institutions, and the technologies developed over the course of the capitalist era would become fundamentally worthless, forcing societies to regress to a less advanced technological state.


Prompt: To illustrate Mises’s point, let’s take a simple example: the manufacture of a pencil. The manager of a pencil-making factory has to make many production decisions, because there are many ways to make a pencil out of its component parts. How does a pencil maker decide how to produce his “final good,” the pencil, out of all the possible “intermediate goods,” the various types of graphite, wood, paint, and other things that go into making it?  In a capitalist society, he begins by checking the price catalog, where he discovers that graphite A costs 35 cents per pound, while graphite B costs 37 cents. If either works, his choice is clear. This manager can perform the same price test for all the relevant inputs, in order to arrive, quickly and accurately, at the most rational way to make a pencil. He does not need to understand how all the activities of society add up to an overall economy.  Prices allow the pencil makers to quickly set aside numerous procedures for making pencils that would result in functioning pencils, but at the cost of squandering natural or labor resources better employed elsewhere. If given tons of the finest quality Cocobolo or Osage Orange lumber, the pencil makers could undoubtedly make good pencils. But this would be a waste if some other tree, like the humble cedar, provided lumber that worked just as well.  Of course, the prices that pencil makers use to make production decisions are not just random numbers. They are expressions of a living market society, characterized by decentralized decision-making, involving large numbers of producers and consumers. Markets place pressure on all producers to get prices right. If it proves possible, for example, to make pencils more cheaply without sacrificing quality by using a new technique, the firm that does so will earn a sizable profit. New information about pencil production possibilities will show up in the system as a lower pencil price.

Response: Each producer can make rational decisions about what and how to produce, only because a struggle for market supremacy forces producers to maximize their revenues and minimize their costs. All of these market-dependent producers absorb information to the best of their abilities, make decisions, and take risks in search of new production possibilities and the corresponding monetary rewards. Socialist planners couldn’t possibly reproduce such a complex system, Mises believed, because they would never have more information than market participants mediated through the price mechanism.  Ultimately, prices tell producers which production possibilities have any chance of turning a profit. Without prices, Mises argued, the rational allocation of assets becomes impossible.  Fatal Errors What’s striking about Mises’ description of capitalism is that it is already highly algorithmic. In his account, the managers of the pencil factory behave like a computer program. They collect price information about intermediate inputs and then follow a simple rule: choose the cheapest option for each input that does not lengthen production time or lead to an unacceptable reduction in demand.  Many socialists responded to Mises’s challenge by accepting his basic premise and then trying to write their own algorithm. In other words, they wanted to show that planners could create a substitute for the price system that could generate enough information to arrive at the correct production decisions for a socialist society.  The Polish economist Oskar Lange and the Russian-British economist Abba Lerner were the first to develop this idea. Their proposals, worked out over the course of the 1930s and 1940s, involved socialist planners “feeling” their way towards the right prices through trial and error. For example, planners might set the price of an intermediate good required to make a pencil, and then adjust that price as necessary, until the supply of the final good matched consumer demand. A series of approximations would get closer and closer to the true result, much like a computer calculating pi through a sequence of slight additions or subtractions.


Prompt: When Lange and Lerner were writing, modern digital computing didn’t exist. But at the end of Lange’s life, as computers emerged, he discussed the possibility that they could perform this price-guessing work far better than humans. This line of thinking has been taken up by contemporary digital socialists, who point to developments in applied mathematics as evidence that we could do away with the price system, calculating optimal allocations of resources with advanced forms of programming instead.  After all, we have more data than ever before, as well as an unprecedented amount of processing power with which to perform computations on that data. Gigantic firms like Walmart and Amazon are already using advanced algorithms to put all this data to work to plan their internal operations. So, can the promise of algorithmic socialism finally be fulfilled? Not so fast. Advocates of algorithmic socialism misunderstand Mises’s position in the socialist calculation debate, and thus fail to respond adequately to his criticisms. For Mises, the challenge is how to allocate intermediate goods to producers of final goods. That’s not something companies like Walmart and Amazon do, for the simple reason that these companies distribute goods rather than make them. The firms supplying pencils to Amazon and Walmart still rely on market signals to figure out the best way to make their product.

Response: As Mises’s student Friedrich Hayek later emphasized, an economy is not a set of equations waiting to be solved, either with a capitalist price system or a socialist computer. It is better understood as a network of decision-makers, each with their own motivation, using information to make decisions, and generating information in turn. Even in a highly digitally mediated capitalist economy, those decisions are coordinated through market competition. For any alternative system to be viable, human beings still need to be directly involved in making production decisions, but coordinated in a different way.  As Hayek observed, running a business involves practical reasoning, acquired through years of experience. To reproduce the work of the manager of a pencil factory, a planning algorithm would have to know not only about the supply and demand for each type of graphite used in pencil making, but also about the detailed implications of choosing one type of graphite over another in that particular production location, with its specific machines and workforce. It is possible that one could formalize all of this knowledge into explicit rules that a computer could execute. However, the difficulties involved in articulating such rules across all workplaces, in all sectors, are simply staggering.


Prompt: Mises and Hayek were correct to observe that people’s participation in decision-making will remain essential for any economy to function. Yet their vision also sets strict limits on who has the opportunity to exercise this agency. In capitalism, the people involved in making production decisions are managers. They represent only a small fraction of the total number of people involved in production, and they do not need to consult all of those other people when making decisions—except insofar as they are forced to do so by law or contract.  Managers are therefore free to pursue economization within broadly defined limits. If their decisions require that large numbers of workers in a particular town lose their jobs—because the pencil factory is being moved to a place with lower labor costs, for instance—then that is a decision the manager can make without answering to the townspeople. For the market to function, therefore, decision-making power must be concentrated in relatively few hands.

Response: In a socialist society, however, the entire population would control production. Decision-making power would be democratized, and this would almost certainly lead to different kinds of decisions being made. Should people begin to run their own workplaces, they would likely decide to introduce all sorts of changes, such as those related to working conditions, for instance, or to how tasks are organized and assigned. Efficiency, whether calculated in terms of energy use, resource consumption, or labor time, would remain a concern, but it would no longer be the sole concern. It would simply be one of many. Other considerations—dignity, justice, community, sustainability—would also enter the picture.  These other considerations could not easily be absorbed into a one-dimensional optimization algorithm, however, for the simple reason that there is no reliable way to reduce them all to a single, quantitative unit of account. Even natural units, such as tons of iron or grams of penicillin, would prove inadequate. What is the natural unit of justice? Given these constraints, the most advanced computer on the planet still could not determine the correct production plan because the different choices are rooted in competing values and visions of the good—in other words, they are political choices.  If socialist planning is purely algorithmic, it executes decisions in a similar way to capitalist firms. It reiterates the logics of capitalism in a different register: what matters is the extraction of the relevant quantitative information from the mess of qualitative life. But it is only in this mess that the content of socialism can be found.


Prompt: Crafting the Protocol How can a greater variety of qualitative goals become part of the planning process, to be pursued for their own sake? To answer this question, we need to turn to the work of Viennese philosopher Otto Neurath.  Neurath was one of the original targets of Mises’s 1920 broadside against planning. He is remembered today as the theorist of total planning—a phrase that incorrectly conjures the image of social engineers running the economy from a control room. Nothing could be further from Neurath’s vision. On the contrary, Neurath argued that a socialist economy would have to be highly democratic—precisely because it could not be purely algorithmic.  For Neurath, the algorithmic character of the price system was a problem to be overcome, rather than something that socialists should try to replicate. In a capitalist economy, managers are able to make clear-cut decisions about cost-effectiveness only because they are allowed to ignore all of the non-economic costs of their decisions, which include destroying communities, immiserating workers, depleting non-renewable resources, and filling the world with garbage. Economically rational decisions at the level of the firm add up to an increasingly irrational society.

Response: Instead of just optimizing for efficiency, then, socialists need to figure out how to incorporate multiple qualitative criteria directly into their planning mechanism. The issue socialists face is not quantification as such. They could probably quantify many of the criteria relevant to their production process—establishing indexes of sustainability and safety, for example. But to distill all such relevant indicators to one unit of account suggests a degree of commensurability between goals that is exactly what socialists would want to overcome.


Prompt: A capitalist society that wants to reduce pollution needs to set legal limits on how much each factory can pollute, allowing those firms to continue to optimize their production strategies, but under new restrictions. That, in turn, creates incentives for pencil factories to get around those restrictions—and if they can figure out how to pollute without getting caught, those firms can make large profits. By contrast, a socialist society would want to take pollution reduction as a goal to be pursued for its own sake. It would look for ways not just to limit pollution at the pencil factory but to positively improve the environment—increasing air quality, planting trees, and so on—wherever doing so does not rule out the pursuit of other goals.

Response: Such an approach requires far more than mere optimization. Rather than trying to convert all of the qualities and quantities of life into a unifying metric that can be algorithmically optimized, we need to find a way to deal with those qualities and quantities on their own terms. We need to be able to make planning decisions on the basis of multiple, incommensurable criteria, and to coordinate these decisions across society. To do this, we must have agreed-upon procedures for making such decisions collectively—protocols.  There are many ways to design a planning protocol. It could be as simple as a population-wide vote, with the majority deciding the outcome. Or it could take the form of a complex bidding procedure, like an auction. A protocol could even be a game, with a set of rules that specifies who can play, what actions each player can take, and what real-life allocations result from different outcomes. There are many possibilities, but the unifying theme is the need to to craft protocols that allow actual human beings to make holistic decisions that take a variety of criteria into account.  Neurath laid out his version of a planning protocol—a term that he did not himself use—in “Economic Plan and Calculation in Kind,” an essay he wrote in 1925. Planning begins with expert planners reducing the “unlimited number of economic plans” that are “possible” down to a few “characteristic examples.” These planners do the algorithmic calculations, which clarify the options among which people must decide. People are then presented with these options for direct comparison. They evaluate a few different plans across multiple criteria and decide which they prefer: listening to comments, voicing their concerns, and taking a vote.


Prompt: Neurath believed that such a process would enable a particular kind of rationality to emerge. Even where it proves impossible to make clear and precise calculations, he argued, we can still decide rationally. However, the rationality we deploy will be a practical and political rather than purely algorithmic. People will have a chance to voice both their concerns and their desires, before arriving at collective decisions about how to shape, constrain, and direct the production process. They will balance how much they want to consume against how much they want to work. They will weigh their need for energy to heat their homes and power their workplaces against values of ecological sustainability and intergenerational justice. They will decide how much of their time and resources would be set aside for expanding or transforming production and how much for cultural, athletic, and intellectual activities.

Response: In Neurath’s model, decisions made collectively, at the highest level, would then filter down through the rest of the economy, to be implemented across various industries and workplaces. But how would that work exactly? How are local production decisions made? What happens if conflicts or collisions arise—for instance, between the decisions of society as a whole and the demands of workers in pencil factories, producing goods to meet society’s needs?   These complexities suggest that what we need is not one society-wide protocol but many protocols—many structured forms of communication that enable people to reach decisions together. Algorithms would have an important role to play. They would codify what philosopher John O’Neill describes as “rules of thumb, standard procedures, default procedures, and institutional arrangements that can be followed unreflectively and which reduce the scope for explicit judgements,” streamlining the planning process so it doesn’t become an endless series of meetings. At the same time, we would need some set of rules for how to tie all of the protocols together, and to integrate them with the algorithms, in order to create a unified planning apparatus based on software that is easy to use, transparent in its outcomes, and open to modification.  After all, even if we incorporate qualitative goals into our planning, we still have to solve the socialist calculation problem. Producers still have to make decisions that add up into a coherent production plan.  Freely Associated Producers Neurath’s emphasis on democratic decision making was essential. But by proposing the idea of the protocol, he raised more questions than he could answer, especially with the limited technologies available to him at the time. Towards the end of his life, Neurath spent years trying to determine how semi-literate peasants and urban workers could be incorporated into a planning protocol, via the distribution of simple graphical representations that he called isotypes.


Prompt: Today, literacy is widespread across the world, and cell phones are common even in remote areas. The possibilities for protocol socialism are correspondingly enlarged. However, true democratic decision-making about production cannot simply be a matter of a perpetual social-media plebiscite scrolling across one’s phone screen—for the simple reason that many individuals lack the practical knowledge necessary for making most production decisions.  Participation in making each decision, therefore, generally needs to be limited to those involved in and affected by each decision being made, with only decisions that concern everybody being brought to society as a whole. Coordination should take place, in other words, mostly within and between associations. These associations might be composed of producers, consumers, or other groups of people with common identities and interests.

Response: Neurath saw this future dimly, through the lens of the social mobilizations of his time. During World War I, masses of workers joined militant rank-and-file movements demanding workplace democracy, including the Industrial Workers of the World in the US, the Shop Stewards Movement in the UK, the councilists in Germany, and the anarcho-syndicalists in Spain, France, and Italy. An issue that arose in these organizations was how to coordinate production among worker-controlled workplaces. Too often, theorists turned to market prices or price-like labor-time calculations for the answer, anticipating the later Lange-Lerner model of an algorithmic socialism.  Neurath hoped that councils, guilds, and other associations could find another way forward. In particular, he speculated that they might be able to use planning protocols to make their own direct comparisons between different “ways of working”—taking into account many and varied criteria that could not “be reduced to one single unit”—while collaborating with one another to help fulfill society-wide goals.  Today’s digital technologies might make it easier for such comparisons and collaborations to occur. The association of pencil producers might be algorithmically assigned tokens or “points”—as in economist Daniel Saros’s model of digital socialism—which the association uses to bid on graphite, wood, and other intermediate goods, in an effort to find the best way to make a pencil. Periodically, the association of pencil makers would then meet with other graphite-consuming associations. They would examine existing allocation patterns, consider larger social goals, and alter the graphite allocation protocol accordingly. What would otherwise have been an impossibly long, if not interminable, series of meetings might become, with the help of algorithms and protocols, something more manageable—a streamlined planning process, capable of undertaking complex multi-criteria adjustments.


Prompt: From any given starting point, the socialists of the future might then begin to alter the overall shape of their productive apparatus. For instance, they might set out to reduce the work week by 10 percent over five years, without a significant loss in productive capacities. Associations of workers and consumers would then consider the options available to them for enhancing productivity levels in the specific areas that concern them. New technologies might improve labor productivity in pencil factories, but require more rapid depletion of forest reserves. Meanwhile, a new way of organizing hospitals might result in less work for nurses, but at the cost of lower-quality elder care. Where do different associations of workers and consumers stand on these issues?  Associations would make recommendations and reach decisions through the direct comparison of plan options, considering the consequences that each productivity-enhancing innovation would have for other issues that their members care about, such as sustainability and justice. At a certain point, a committee might compare society-wide goals of work reduction to actual achievements, looking at sticking points, theorizing solutions, and adjusting incentives to prioritize certain kinds of labor accordingly.

Response: From this perspective, it is easy to see that a planning process would not emerge fully formed with the push of a button on an algorithmic dashboard. Nor would production be constantly revolutionized—at the cost of dislocating human lives and destroying the environment. Instead, step-by-step adjustments would make the production process ever more rational—in the Neurathian sense, not the capitalist one—across a wide variety of criteria. People themselves would propose, debate, and implement improvements for themselves.


Prompt: The productive apparatus would have more in common with a “food forest” than a factory—a garden of edible plants, tended for hundreds of years and designed to provide for a multiplicity of needs, spiritual as much as material. It would connect the past to the future, across generations. It would be a common inheritance that made it possible for the masses of humanity to live and work as they wanted. Beyond this shared realm of mutual obligations, an enlarged realm of freedom would progressively open up space for radical experimentation that could be explored by all, without endangering anyone’s material security or individual freedom.

Response: A Dance Club for Pencil Makers Digital technologies will assist in the construction of a socialist society, but the role they will play needs to be clarified. We do not want software to substitute for the price mechanism. No matter how digitally mediated a socialist society becomes, it will never be able to escape the need for democratic deliberation at all levels. Human beings are never simply rule followers. They look beyond the rules, sometimes for social benefit, sometimes for personal advantage, and often for both.  At the same time, we have to accept that deliberating endlessly is undesirable and doomed to failure. To function at all, a society that replaces the single-minded focus on cost control with multi-criteria decision-making must use algorithms to help clarify the choices to be made and protocols to help structure the way it makes these choices. We cannot rely on a single, unified mechanism for this purpose; we will need many. And open-ended debate must modify these mechanisms when they generate bad results or threaten to give rise to new forms of domination.  In designing our protocols and our algorithms, it is crucial to remember that the point of this process of social transformation is not only to make work better, but also to work less. Too often, socialists have seen work as the highest realization of human freedom. In truth, work will never be an entirely free activity. But in a world no longer beholden to the capitalist growth imperative, advanced technologies can substantially reduce the amount of work demanded of any individual. With greater free time and available space, all individuals will be able to develop their personalities outside of a work-centric identity.  The world’s pencil makers would be free to invest themselves in a much wider range of ends, whether starting specialized gyms or dance clubs, joining theatre troupes, or forming amateur scientific societies. A rich and varied life beyond work is only possible if work is organized in a way that is fair, rational, and resistant to whatever forces might emerge to subjugate human beings once again. Instead of waiting for a breakthrough in artificial intelligence to achieve this goal for us, we should begin to develop the protocols of the future today.


Prompt: The Los Angeles Police Department’s Real-Time Analysis and Critical Response Division (RACR) is housed in a hulking, institutional-gray building about a mile north of downtown. Its only marking is its address: 500. In the fall of 2013, I had an early morning meeting there with Doug, a “forward-deployed” engineer from Palantir Technologies, which builds and operates one of the premier platforms for compiling and analyzing massive and disparate data used by law enforcement and intelligence agencies. He was one of about eighty people I interviewed over the course of five years to understand how the LAPD uses Palantir and big data.

Response: Palantir’s goal is to create a single data environment, or “full data ecosystem,” that integrates hundreds of millions of data points into a single search. Before Palantir, officers and analysts conducted mostly one-off searches in siloed systems: one to look up a rap sheet, another to search a license plate, another to pull up field interview cards, and more still to search for traffic citations, access the gang system, and so on. Seeing the data all together in Palantir is its own kind of data.  Palantir’s clients—federal agencies such as the CIA, FBI, Immigration and Customs Enforcement, and the Department of Homeland Security; local law enforcement agencies such as the LAPD and New York Police Department; and commercial customers such as JPMorgan Chase—need training in order to learn how to use the platform, and they need a point person to answer their questions and challenges. That’s where engineers like Doug come in.  In a training room at the RACR, I watched as Doug logged in to Palantir Gotham, the company’s government intelligence platform, and pulled up the homepage (Figure 1). For him, it was a banal moment, but I’d been eagerly anticipating this sight: there is virtually no public research available on Palantir, and media portrayals are frustratingly vague.


Prompt: Palantir homepage. Source: Los Angeles Police Department. Doug started running through some of the different ways the platform could be used. He patiently explained what he was doing as he queried, clicked, zoomed, narrowed, and filtered: So now, imagine a robbery detective who says, “Hey, you know what, I have a male, average build, black four-door sedan.” Like, they would [previously be able to] do nothing with that, right? So, we can do that.Let’s go take a look at vehicles that are in the system… There are 140 million records in this system … we know it’s a Toyota, maybe a Hyundai, right? Or a Lexus… So let’s say we think it’s one of those types of vehicles, right? And that got us then to 2 million [vehicles]. And if we were to go look at, say, a color … we know it was black. Maybe it was blue, ’cause it could have been blue. It could be dark green… And we know it was a four-door.Do you see what’s happening over here? In five hops, they’re able to get down to 160,000. Now they’re still not going to look at 160,000 vehicles. We didn’t get into model and year, but we could do that, and we could chart it, which makes it easy… So now I could say, I think it was between 2002 to 2005, drill down, now we’re 23,000. Now it gets pretty manageable.		So now let’s flip over and let’s look at the people that are connected to these vehicles. And I know I’m looking for a male. And I’ll just do one of them.And I know that, like, let’s say he was pretty short. And he was on the heavier side. Brick house. We just got down to thirteen objects, thirteen people. And you could say, “Okay, well, now let me take a look at—all thirteen have driver’s license numbers.” So now we’ve narrowed it down to thirteen potential people, and they could take these thirteen objects and go to the DMV and pull their DMV photos and go to the witness or victim and say, “Here you go.”	 In less than a minute, using partial information, Doug was able to narrow a search from 140 million records to thirteen. He went on to show me how to look up which of the thirteen had any citations or arrests, the LAPD divisions in which they received their citations or were arrested, and identify one person who had been cited in the same division in which the robbery occurred. If the person ended up not being the person who committed the robbery, officers could save this search formula and keep running it in the coming days, in case any new data came in.

Response: I asked what happens when the system gives a false positive. What happens to the wrong suspect? Doug said bluntly, “I don’t know.” One Person’s Click Is Palantir’s Clue We all leave hundreds of digital traces—clues, should it come to that—every day. When we use our cell phone, run an internet search, or buy something with a credit card, we leave a digital trace. Rapidly proliferating automatic data-collection sensors record and save those digital traces, and make dragnet surveillance—the collection and analysis of information on everyone, rather than only people under suspicion—possible at an unprecedented scale. Individuals with no direct police contact are now included in law enforcement systems, and police now collect and use information gleaned from institutions, like credit-rating agencies, that are typically not associated with crime control.


Prompt: But data—particularly large, diverse sets of data—are relatively useless on their own. You need a good platform to sort through them. And Palantir is excellent at processing, sorting, and analyzing data. With the right platform, searches that used to take hours, days, or even weeks may now take only a few seconds. Dragnet surveillance—and the data it produces—can be incredibly useful for law enforcement to solve crimes. As one officer explained, after any crime, “the first thing you’re gonna do, always, is check the digital footprint.” People working in information technology have a vested interest in making the case that information technology is a crucial component of law enforcement. But no matter how you quantify it—through increased federal and within-department funding for data-intensive policing, the proliferation of law enforcement contracts with tech companies, the increase in tech-training sessions for police, or the rising number of data points the police access daily—data analytics are central to law enforcement operations today.  The Southern California fusion center Joint Regional Intelligence Center (JRIC), a multiagency, multidisciplinary surveillance organization, started using Palantir in 2009 to connect and analyze Suspicious Activity Reports (SARs). At the time, it was the largest law enforcement deployment of this software anywhere in the world. The LAPD, Long Beach Police Department, and LA City Fire Department soon adopted the platform, and there were over 1,300 trained Palantir users in the region by 2014. More users are onboarded every month. A sergeant named Michaels, who coordinates some of the training sessions at JRIC, claims “they catch bad guys during every training class.” The LAPD’s arrest records and field interview cards—small, double-sided index cards that officers fill out with key information about people they interact with in the field—were the first data sources integrated into Palantir. Both are geocoded, meaning you can plot where these police stops and arrests occurred. Palantir does not own the data the LAPD uses, but rather provides an interface that makes it possible to link data points across previously separate systems. Users can plot data on maps, as a network, as a time wheel, or as a bar graph with a timeline of phone calls and financial transactions, for example. The platform even allows users to organize structured and unstructured data content such as emails, PDFs, and photos through tagging.

Response: Because one of Palantir’s biggest selling points is the ease with which new, external data sources can be incorporated into the platform, its coverage grows every day. LAPD data, data collected by other government agencies, and external data, including privately collected data accessed through licensing agreements with data brokers, are among at least nineteen databases feeding Palantir at JRIC. The data come from a broad range of sources, including field interview cards, automatic license plate readings, a sex offender registry, county jail records (including phone calls, visitor logs, and cellblock movements), and foreclosure data.


Prompt: Though there was a lot of uncertainty among my interviewees about exactly what data were in the databases they were accessing, a pair of civilian employees mentioned the use of LexisNexis’s public records database Accurint and speculated that it contained documents like utility bills and credit card information. Indeed, LexisNexis has over 84 billion public records from 10,000 diverse data sources, including 330 million unique cell phone numbers, 1.5 billion bankruptcy records, 77 million business contract records, 11.3 billion name and address combinations, 6.6 billion motor vehicle registrations, and 6.5 billion personal property records.  The process of labeling and linking objects and entities like persons, phone numbers, and documents makes it possible to plot data on maps and graphs that let users see data in context and make new connections. It can also make it easier to see what crucial data might be missing or what sorts of data might be useful for law enforcement to begin collecting. Whereas one piece of information may not be a useful source of intelligence on its own, Doug explained, the “sum of all information can build out what is needed.” The Danger Imperative Most sworn officers and civilian staffers, including crime analysts, who use Palantir Gotham use it for simple queries (what Palantir calls “drilling down using ‘object explorer’”). Users can search for anything from license plates to phone numbers to demographic characteristics, and a vast web of information will be returned. One officer described the process:	 You could run an address in Palantir, and it’s going to give you all the events that took place at that address and everyone who’s associated to those events… So if it’s a knucklehead location where a lot of things are happening there, you’re gonna get people documented on there one way or the other… Either field interview cards, or they’re on crime reports, whatever… Otherwise you could search all of the records within [LexisNexis’s] Accurint … and see who’s living with who a lot of times.

Response: It’s not quite so simple as “run a query, get a list of suspects.” Notice, for example, how the officer says “knucklehead location”—he means that you can query an address, and if it’s been listed as the address for many people or their car registrations, if it’s been the site of multiple calls for service, or it’s otherwise connected across the databases, a Palantir Gotham search is going to return a tangle of information. Some of it will be useful, some of it won’t, but the presumption is that if criminal activity is going on at a location, someone or something will be in Palantir.


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

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


Prompt: Another way to use the analytic suite is to paint a detailed picture of the population of interest in an area. One officer explained this: The big thing that Palantir offers is a mapping system. So, you could draw out a section of [his division] and say, “Okay, give me the parolees that live in this area that are known for stealing cars” or whatever [is] your problem… It’s going to map out that information for you … give you their employment data, what their conditions are, who they’re staying with, photos of their tattoos, and, of course, their mugshot. [And it will show] if that report has [a] sex offender or has a violent crime offender or has a gang offender. Some are in GPS, so they have the ankle bracelet, and … we have a separate GPS tracker for that.

Response: A Palantir software engineer spoke of the gang unit monitoring entire networks of people: “Huge, huge network. They’re going to maintain this whole entire network and all the information about it within Palantir.” Palantir, one sergeant explained, is also an “operational game changer”: it gives him the data he needs to protect his officers’ safety by, for instance, locking down a neighborhood and positioning an airship overhead while law enforcement conducts a search. Of course, this situational awareness made possible by Palantir can ratchet up officers’ sense of danger and escalate an already tense situation. Such platforms provide an unprecedented number of data points supporting the “danger imperative,” or the cultural frame officers are socialized into, which encourages them to believe that they may face lethal violence at a moment’s notice.


Prompt: Criminal Justice Creep New data sources are incorporated into Palantir regularly. One captain commented:				 I’m so happy with how big Palantir got… I mean it’s just every time I see the entry screen where you log on there’s another icon about another database that’s been added … they just went out and found some public data on foreclosures, dragged it in, and now they’re mapping it where it would be relative to our crime data and stuff.

Response: Another interagency data integration effort is LA County’s Enterprise Master Person Index (LA EMPI) initiative. If established, LA EMPI would create a single view of an individual across all government systems and agencies: all of their interactions with law enforcement, social services, health services, mental health services, and child and family services would be in one place under a single unique ID. Although the explicit motivation behind the EMPI initiative is to improve service delivery, such initiatives extend the governance and social control capacities of the criminal justice system into other institutions.


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

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