Prompt: The act of gerrymandering acquired its name in 1812, when Massachusetts governor Elbridge Gerry approved a state redistricting plan that favored his own party. The partisanship of the map was manifest in the contorted shapes of its districts—one in particular, the story goes, reminded the editor of the Boston Gazette of a salamander, giving rise to a portmanteau that stuck: the gerry-mander.

Response: The name was new, but the practice was not. By the time the case was making news, gerrymandering had long been an established fact of American politics. Even so, redistricting was a tedious process done entirely by hand. It involved a considerable amount of work: maps had to be drawn and redrawn, demographics had to be tallied and retallied, all within a short timeframe. It also required intricate knowledge of the electorate across the entire state.


Prompt: Digital technology has revolutionized this process. As mathematician Jordan Ellenberg explained in a New York Times op-ed, “Gerrymandering used to be an art, but advanced computation has made it a science.” While drawing districts for partisan gain is an ancient custom in American politics, computers have greatly improved its effectiveness.

Response: Digital technology has advanced to the point where carrying out a legal, effective, and durable gerrymander may hardly be more difficult that editing a picture in Photoshop. The consequences are troubling. Left unchecked, digital gerrymandering threatens to make competitive elections a thing of the past.


Prompt: Packing and Cracking To understand how gerrymandering works, consider a pile of stones, some blue and some red. You have to group the stones into as many piles of three as possible. But you want to arrange the piles so that blue wins—in other words, you want the maximum number of piles to have a majority of blue stones.

Response: Suppose the pile has fifteen stones: nine red and six blue. Even though there are more red stones—red has a three-to-two advantage over blue—this is a combination that can nonetheless be grouped into piles of three so that blue has the overall majority. Forming groups of three blue stones must be avoided, for only two stones are required for blue to “win” a pile. Similarly, it is ill-advised to place a blue stone with two red ones: blue does not win that pile, so again a blue stone gets wasted. The optimal solution for a blue victory is to form three piles of two blue stones and one red stone, and two piles of three red stones.


Prompt: This example captures the essence of gerrymandering. The strategies employed even have specific names: “packing” and “cracking.” “Packing” is when you concentrate the other party’s voters in certain districts—putting the red stones into two all-red piles. “Cracking” is when you dilute the other party’s electoral strength by distributing its voters across many districts—scattering the red stones into three blue-majority piles.

Response: Block Party Whenever you rearrange the stones, you have an opportunity to gerrymander. That happens every ten years, when states draw new Congressional and legislative districts. Redistricting immediately follows each national census, and it is designed to accommodate changing trends in population. A district map is supposed to satisfy several—often conflicting—criteria, such as equal population, stipulations of the Voting Rights Act, respect for city boundaries, and fair representation for “communities of interest”—broadly defined as groups of individuals who have similar legislative concerns.


Prompt: Meeting all of these requirements is highly challenging. As a result, redistricting is ultimately a matter of trial and error. Officials often have to test many different iterations of a district map until they find one with an acceptable approximation of the desired outcome. To conceptualize the redistricting process, imagine that you have a paper map that shows the boundaries of various numerically-labeled “census blocks.” (A census block is the finest level at which census data is reported; in urban areas, it often corresponds to a city block.) You also have at hand the corresponding tabular demographic data—for instance, the race and gender breakdown, or the number of voting-age residents of voting age—referenced by block number.

Response: Now imagine laying a sheet of tracing paper on top of the base map. On this layer, you are free to trace lines around groups of census blocks to form your own “districts.” You can use the census data tables, and some basic arithmetic, to find the demographic information specific to any given district—say, the number of voting-aged Latina women living there. This is the process of aggregating census data to the district level.


Prompt: Visualizing census data by district is invaluable for drawing legal districts. But a gerrymanderer needs more than just census data—they also need to predict election results by census block. They need to know the color of the stones, in other words. Election results, however, are not reported by census block, and methods of collecting and tabulating election data vary greatly by state.

Response: The Voting Tabulation District (VTD) is the Census Bureau’s general term for the “unit” at which election data is reported. A VTD might be a voting district, a ward, or a county, depending on the state. Fortunately, these VTDs are themselves built from census blocks, so that the process of integrating this data involves disaggregating the VTD election data into its constituent blocks.


Prompt: Suppose that in a recent election, a VTD has a voting population of 1000. Of those voters, 600 voted for the Red Party, and 400 for Blue. If one census block within the VTD has ten residents of voting age, that block is projected to be worth six red votes and four blue—the same red-to-blue ratio as the entire VTD. If another census block from the same VTD has fifty voting-age residents, it is projected to be worth thirty red votes and twenty blue.

Response: Repeating this process for every VTD gives each census block in the state its own weights of red and blue votes. This provides an approximation of historical election results at the census block level, which in turn predicts the block’s vote for future elections. The blue-biased gerrymanderer can then group census blocks into districts to maximize the number of projected blue wins, based on the election results that are disaggregated from VTD data.


Prompt: Point and Click Redistricting can be done by hand, but it’s time-consuming and difficult. Fortunately, it’s the sort of task that technology excels at—which is why officials now use standard commercial redistricting software when drawing districts. But the same features that make these applications useful for creating compliant districts are also what make them useful for gerrymandering.

Response: Since the 2001 redistricting cycle, most states have used a GIS software package called Maptitude to draw their district maps. Maptitude has attracted the attention of state officials with its innovative capacity to handle the Census Bureau’s own digital geographic feature files. It also comes prepackaged with demographic census data, which it easily merges with any other data the user chooses to load into the program. The user then works with a layered map, where each layer corresponds to a different type of feature, such as a road or a political boundary—much like a digital version of the tracing paper scheme used to aggregate census data by hand.


Prompt: Using Maptitude is comparable to using Photoshop. The user chooses a map layer—say, the counties layer. With a few clicks of the cursor over the map, the user selects a particular cluster of counties. The program automatically adds the boundary of the group to the working map layer as a new “district.” Statistics about the new district are instantly calculated from the available data and displayed in a table on the screen.

Response: With this instant feedback, the user can then make small modifications to the boundary as needed. For instance, if the population of the district is slightly too small, the user can switch to the VTD layer and attach a single VTD to bump up the district size. With software like Maptitude, redistricting becomes significantly easier—and so does gerrymandering.


Prompt: Big Data Gerrymandering Maptitude doesn’t just make it easier to work with census data, however. It also enables users to incorporate data from other sources that might be useful for gerrymandering. After all, in the age of big data, there is a broad spectrum of information available for purchase, some of which is overtly predictive of the voting behavior.

Response: Credit card purchases can reveal membership in the NRA, regular donations to Planned Parenthood, or subscriptions to the National Review. Geopositioning software on personal devices is becoming more widespread, and often goes unnoticed by users. A geotagged photo taken at a white-supremacist rally, for instance, might reveal important data about that user’s political preferences.


Prompt: All of this information could easily be loaded into Maptitude and used to draw gerrymandered districts. It should be emphasized that the use of this type of commercially available data for the purposes of gerrymandering is, to date, strictly hypothetical. However, so-called “data brokers” are already in the business of packaging vast bundles of data for the purposes of selling it to political parties for campaigns and fundraising. Using it for gerrymandering seems like a natural next step.

Response: Algorithms used by tech companies to sell advertising already do an excellent job predicting consumer preferences. These same algorithms may be just as effective in predicting political preferences. The abundance of data—and the sophistication of the machine learning models that train on that data—make it possible to detect patterns in human behavior that would otherwise be invisible.


Prompt: It’s not simply that algorithms are getting better at predicting behavior, however. It’s also that voting patterns are getting easier to predict, as the US electorate becomes increasingly polarized. An astonishing 66% of states are currently governed by a “trifecta,” in which both legislative chambers and the governorship are held by the same party—the vast majority by Republicans.

Response: Trifectas tend to encourage gerrymandering. When a legislature knows the governor is unlikely to veto a district map—as when the map’s bias works in the governor’s favor—public officials are far more likely to attempt a bold gerrymander. This is precisely what happened in Wisconsin in its last redistricting process in 2011, which provoked a legal challenge now before the Supreme Court, Gill v. Whitford.


Prompt: Drawing Better Shapes Wisconsin’s gerrymandered map all but guarantees a permanent Republican majority, even though the state as a whole is evenly split between Democrats and Republicans. As part of their evidence in Gill v. Whitford, the plaintiffs have presented computer simulations that conclude that the map is an unmistakable statistical outlier, strongly suggesting that partisan interests were at play in its design.

Response: In our current political environment, however, it was not entirely surprising that two Supreme Court justices, including Chief Justice Roberts, dismissed much of the work as “sociological gobbledygook” during oral arguments in October 2017. Stanford professor Jonathan Rodden, contributor to an amicus brief for the case, expressed his frustration to me in an interview: “There are so many things that we can do that are really rigorous and correct. But then you talk to a federal judge and they just look at you like you’re from Mars.” The Supreme Court is expected to issue its ruling in June 2018, and the decision is likely to have significant impact on the future of gerrymandering. But Rodden suspects that the outcome of the Wisconsin case is not as earthshaking as some observers would like to believe. The courts aren’t a silver bullet when it comes to cracking down on gerrymandering—rather, different political approaches are needed.


Prompt: For states that hold referenda, it may be possible to put measures on the ballot that hand the task of drawing districts to an independent commission. This was the path that California ultimately took to address its gerrymandering problem, a move widely acknowledged to have been successful. The state of Florida passed an amendment making gerrymandering explicitly unconstitutional, and it has been used to disqualify a biased district map.

Response: But any enduring solution will require sustained civic engagement. You can’t force politicians to stop drawing maps that help them get elected without building popular pressure from below. Maimuna Majumder, a graduate student at MIT and computational epidemiology research fellow at HealthMap, has observed a negative correlation between gerrymandering awareness and the presence of gerrymandering across the United States. The more people know about gerrymandering, in other words, the less it happens.


Prompt: That creates a sense of urgency when it comes to raising awareness—particularly as gerrymandering becomes more effective with ever more granular data and ever more predictive algorithms. Our democracy depends on it. Everyone knows what gentrification looks like. Community gathering places are replaced by boutiques and bank branches. Corner stores are made obsolete by VC-funded vending machine startups. Blocks that spent decades in a state of disrepair sprout Michelin-starred bistros and cocktail bars—some even trading on the allure of formerly stigmatized neighborhoods with Instagram-ready touches like fake bullet holes and 40-ounce bottles of rosé wine.

Response: These new businesses don’t just reflect gentrification, of course. They actively drive it. Take New York City’s SoHo neighborhood: sociologist Sharon Zukin has described how art galleries and live-work spaces displaced industry from Lower Manhattan and paved the way for luxury residences for non-artists. Or Dumbo: in the late 1970s, real estate developer David Walentas came up for the idea of redeveloping a quiet corner of the Brooklyn waterfront while eating in the area’s lone gourmet restaurant.


Prompt: In recent years, however, technology has accelerated this dynamic. Upscale consumption spaces have always been engines of gentrification. What’s new is how these spaces increasingly attract customers beyond their immediate surroundings: through “location-based services” (LBS), a genre of smartphone and web applications that filter digital information about the world through a user’s spatial coordinates.

Response: Apps like Yelp and Foursquare are not just tools for finding cafes and restaurants in gentrifying neighborhoods, in other words. By making new establishments more visible to upper-income patrons in other parts of the city, LBS are transforming those neighborhoods in very direct ways. Everyone’s a Critic Cities have evolved in tandem with text and image-based information systems designed to help overwhelmed residents choose among competing options for consumption. To take one example, annual publications of business information like the Yellow Pages have existed since the 1880s. But digital technology adds unprecedented speed and precision to this process. Today, companies like Foursquare, Yelp, and TripAdvisor offer continuously updated lists of businesses and attractions with extraordinary specificity.


Prompt: You can find Polish restaurants that take American Express, dog-friendly hotels near Lake Tahoe, and bilingual Mandarin-English notaries. By speeding up the flow of information, LBS turn cities into what mainstream economists consider more “efficient” markets, tightening the connections between critical evaluation and real estate value in a process that can help accelerate gentrification.

Response: Imagine moving to New York City or London in 1970 and deciding where to spend your free time. Most of your knowledge of the urban scene would come from personal experience, conversations with friends, family, and coworkers, or via articles, reviews, and ads in newspapers or magazines like New York or Time Out. If a new restaurant or art gallery opened elsewhere in the city, your likelihood of finding out about it would be limited by the publication schedule and capacity of the local media; many smaller establishments would never be “newsworthy.” This is precisely the problem that Manhattan lawyers Tim and Nina Zagat wanted to solve in the late 1970s, when they asked their friends and colleagues in New York’s rapidly expanding professional services sector to rate various restaurants in the city. After being passed around among thousands of Citibank employees like samizdat literature for yuppies, the Zagats turned their survey into a wildly popular retail guidebook, ultimately expanding to over seventy cities. Rather than using professional dining critics, the Zagat Survey drew its judgments from hundreds of thousands of individual diners. This amateur contributor pool fed data into the company, which synthesized it into statistical averages that they sold in pocket-sized books by the millions.


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

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


Prompt: With smartphones, a new level of scale became possible. The number of users contributing data could be far greater than anything imagined by Tim and Nina Zagat—and the sheer volume of that data could be greater as well. Mobile devices also added a spatial element: users could constantly broadcast their location, enabling more geographically specific interactions.

Response: The current culmination of Crowley’s efforts is Foursquare City Guide, a mobile app that provides personalized recommendations based on a user’s location and past interests. These recommendations are themselves sourced from other users, in the form of venue ratings and reviews. The company touts its use of machine learning to sort through all this information and match users with venues. Foursquare might recommend a particular coffee shop to you not just because it’s nearby and highly rated, in other words, but because the algorithm believes you will like it—based on your demographic profile, your clickstream of past likes, the time of day, and so on.


Prompt: The scale is impressive. Formerly, Foursquare enabled users to “check in” to a particular location—essentially telling the app, “I am here.” Since Foursquare’s launch in 2009, users have performed 12 billion of these check-ins across over 8 million unique locations. But the company no longer needs users to announce their location—Foursquare has the technology to track users through GPS coordinates, cell towers, and Wi-Fi signals. Simply by walking around with a smartphone, the Foursquare user makes the company’s multidimensional map of urban space incrementally more useful.

Response: The ultimate goal, Crowley says, is to create the “location layer of the internet.” He wants to “crawl the world with people in the same way that Google crawls web pages with machines.” Just as Google crawls the web in order to organize it for use, Foursquare crawls the city in order to organize it for consumption.


Prompt: Serfs Up But there’s a crucial distinction: whereas Google’s crawling is automated, Foursquare’s is performed by actual humans—a fact that LBS companies tend to downplay. A lot of the hype around urban information systems centers on “artificial intelligence”: the ability for companies like Foursquare to seamlessly guide you to relevant places and products. But as danah boyd and Kate Crawford, among others, have pointed out regarding “big data” more broadly, these apps actually derive their value from a vast stockpile of human labor.

Response: And, as with any labor pool, some workers are more valuable than others. Certain Foursquare users are more passive in their interactions with the app; others actively contribute ratings and reviews, and check in to new businesses using Foursquare’s “lifelogging” app Swarm to keep the company’s data current. According to Foursquare’s own promotional material, this population of “superusers” is only 43,000. But this relatively small cohort, performing what Tiziana Terranova calls the “free labor” undergirding the online economy, produces a large portion of the company’s value.


Prompt: The aggregate product of all this labor is extremely valuable for Foursquare’s advertisers and investors, at least in theory. The company is reportedly on track to hit $100 million in annual revenue in 2018, and its 2016 fundraising round gave it a valuation of $325 million. By industry standards, this isn’t even particularly high: its rival Yelp, which is publicly traded, has a market capitalization of over $3.5 billion. Google purchased Zagat in 2011 for $151 million, after infamously failing to acquire Yelp, and both Apple and Facebook have their own proprietary ratings of real-world businesses.

Response: Schrödinger’s Latte Power users and reviewers don’t just create value for companies like Foursquare, however. Their labor also enriches the developers, brokers, landlords, and small business owners that profit from the gentrification of urban space. That’s because LBS don’t just measure and map the city—they transform it.


Prompt: By making it easier for urbanites to search for gluten-free pasta or private room karaoke, businesses can draw an audience from farther afield, especially in conjunction with the rise of ridesharing services like Lyft and Uber that efficiently ferry well-heeled patrons around the city. And by opening up a “long tail” of establishments for review, LBS enable even the humblest coffee shops to have thousands of reviews.

Response: If one of these coffee shops becomes popular, it can have significant consequences for surrounding real estate values. In a capitalist real estate system in which property values reflect both scarcity and desirability, changes in a neighborhood’s amenities are quickly reflected in rising housing prices. Real estate brokers understand this dynamic, and actively cultivate it. In places like Harlem, they have even opened their own cafes in order to expand the gentrification frontier to areas where they have listings.


Prompt: Brokers often enlist LBS directly to sell particular neighborhoods. Online real estate listings include “Walk Score” ratings calculating nearby amenities using LBS data. Real estate platforms like Trulia directly incorporate Yelp data into their listings pages. These digital urban information systems, then, don’t just gentrify neighborhoods by funneling customers to upscale establishments. They also provide a valuable source of data to the brokers who hope to convert those customers into residents.

Response: Brunching While Rome Burns Crawling the city is lucrative for technology companies, the real estate industry, and other beneficiaries of gentrification. But what happens when the users who crawl the city can no longer afford to live there? What happens when services like Foursquare and Yelp lose their labor supply? The long-term profitability of these companies is threatened by the growing impoverishment of their key user base: urban millennials. Squeezed by stagnant wage growth and sky-high housing prices, both lambasted for their thrift and shamed for small indulgences like avocado toast, this cohort’s ability to generate new data and justify advertising campaigns is crucial to the location data economy. And while LBS founders and investors skew predictably straight, white, and male, prolific Yelp users in cities like New York and San Francisco are more likely to identify as female and Asian-American, facing a double bind of discrimination despite educational credentials well above average.


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

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


Prompt: Now a more muddled, sobering picture is coming into view. As a handful of platforms come to dominate the internet, consolidation and commercialization are putting limits on linguistic variety. Google’s claim to “organize the world’s information” and Facebook’s bluster about “bringing the world closer together” ring hollow when these things only happen in around 5 percent of the world’s languages.

Response: At the edges, or in the interstices, of the official, searchable internet—that’s where the languages are. Click away from the shiny, texty, corporate pages where at most just a few hundred languages flourish, the biggest handful way out in front, and you can chase traces of the world’s actual, vanishing linguistic diversity, some 7,000 languages deep.


Prompt: How that diversity holds up in the face of new and evolving technologies will depend on their design (how they handle orality, for instance), their architecture (e.g. how open they are), and on the efforts of individuals and communities. Smaller languages and cultures stand a chance if they can organize, muster a critical mass of users, reproduce, and meet and mingle with other cultures on an equal footing.

Response: Not just online, but offline too—where urbanization, analogous to the ingathering of the internet, is challenging the assumption that smaller cultures need isolation to survive. Today’s internet, likewise, is a clamorous site of last-minute linguistic life and death: a “Babel in reverse.” Groups don’t need to wall themselves off—hardly possible now anyway—in order to preserve and develop their heritage. But they do need rights, resources, and respect to manage the transition from traditional territories to diasporic possibilities.


Prompt: Tearing Down the Tower of Babel, Again At the Endangered Language Alliance (ELA), physical and digital diasporas are our point of departure. Our home base of New York City, with up to 800 languages spoken in the metropolitan area, is the most linguistically diverse place in the history of the world—precisely and paradoxically at the moment when more and more languages are disappearing.

Response: To take one example, as many as one-fifth of the world’s languages originate in the greater Himalayan region that stretches from northern Pakistan across to Vietnam. Within two generations, fueled by the occupation of Tibet, climate change, and diminishing economic opportunities, a new Himalayan diaspora has taken shape. This diaspora is increasingly digital and urban, rooted in cities like Kathmandu, Dharamsala, Delhi, and New York.


Prompt: Speakers of Himalayan languages—who also use the English, Nepali, Tibetan, Hindi, or Chinese internets—are forming their own WeChat and Viber groups, hundreds of members strong, making room for local, oral vernaculars like Amdo Tibetan, Sherpa, and Mustangi. They are creating projects like ELA’s Voices of the Himalayas, a series of oral history videos watched across the diaspora, featuring a dozen Himalayan languages rarely seen or heard online before.

Response: Crucial, along with the spread of smartphones and social media, is the new ease of recording and sending audio messages, sometimes video too, within group text streams. Most of the world’s languages were traditionally oral and unwritten. Many still have no script, but the newly widespread literacy in larger languages and the ease of texting are prompting more and more speakers of endangered languages to experiment with writing their languages. Some have started battling with ICANN, Unicode, Microsoft, and the like—the gatekeeping language academies of the digital age—to support non-Latin scripts.


Prompt: Remixing, reusing, and readapting dominant-culture content is another major strategy for smaller languages, accelerated by the web. A whole universe of diasporic digital media now exists online: Tom and Jerry cartoons dubbed into otherwise invisible Chinese languages; Seinfeld in Yiddish; online television in Inuktitut, a major Inuit language; scenes from Troy, Titanic, and other Hollywood blockbusters, mischievously and hilariously voiced-over in southern Italian “dialects” very different from the Tuscan-based standard. (“Troy in Altamurano” has over 430,000 views; the town of Altamura has just over 70,000 inhabitants.) YouTube alone, if you know what to look for, contains the flotsam and jetsam of a thousand languages. But it remains a long road from those phone-captured fragments and remixed movies to what sociolinguists call a whole new language “domain”—especially at a time when smaller languages are ceaselessly ceding traditional domains like work, education, and religion to larger languages.

Response: Maybe the Apps Will Save Us While threatened but still vital languages, concentrated in the Global South, wage a grassroots digital struggle to be heard, languages on the brink or already silent—concentrated in North America, Europe, and Australia—are counting on a tech-led resurrection from above. “Can an App Save an Ancient Language?” “Can a Podcast Revitalize an Endangered Language?” “Video Game Offers New Life to Ancient Indigenous Language”—these are constant headlines now: positive but counterintuitive, dramatic but non-threatening. Linguists, teachers, and activists, many now employed by tribes and communities, share tools in forums like the Indigenous Languages and Technology (ILAT) listserv run by Phil Cash Cash, a scholar and speaker of Nez Perce from northeastern Oregon. Tribes like the Chickasaw Nation, headquartered in Ada, Oklahoma, are creating sophisticated iPhone and Android apps that aim to preserve the Chickasaw language by teaching the alphabet, along with basic words and phrases, and providing recordings of the remaining native speakers.


Prompt: In parallel, beginning in the 1990s, many academic linguists—awakening to the disappearance of the very basis of their field—began a substantial push to document smaller languages. One driving force was the availability, for the first time, of relatively cheap, portable, and easy-to use video and audio equipment capable of producing archive-quality recordings. Gathered primarily for academic purposes in digital repositories like the Endangered Languages Archive and Documentation of Endangered Languages (DOBES) collection, these represent a significant but largely frozen legacy, aimed as much at tomorrow as today.

Response: They may be online, but most of the corpora gathered by linguists in the field, like the peoples whose voices they represent, are still too small, poor, and particular to interest the likes of Google and Facebook. Smaller languages may only have whatever material one linguist, often a graduate student, can muster on a short-term grant. How accessible such recordings can or should be on the wider internet remains an open question, where communities differ.


Prompt: By contrast, big languages have big corpora: annotated text collections gathered by governments, companies, or researchers that allow for all sorts of data analysis. Google Ngram Viewer, for instance, lets users search printed sources over time in a dozen larger languages, like English, Chinese, and Spanish. New technologies and platforms, dependent on capital under the current system, are inevitably developed with big markets, communities, and languages in mind, leaving language activists to push their way up through the digital cracks.

Response: Do Androids Dream of Small Languages? Dreams of a magic bullet for languages persist, from Douglas Adams’ “Babel fish” to the real-time translation promised by Google’s new Pixel Buds. Online language teaching, universal machine translation, enhanced voice recognition—these are just a handful of possible cyborgian fantasies. Nor is it unthinkable that new techniques of capture and simulation through technologies like virtual reality could make the past effectively eternally present, in some kind of linguistic parallel to the push for indefinite life extension, cloning, and the de-extinction of biological species. The annihilation of space by time may be followed by the annihilation of time altogether—and the past, as we’ve traditionally known it.


Prompt: In the meantime, there is the risk that futurist fantasies will distract us from developing strategies grounded in the everyday lives of communities of speakers, especially the smallest and most threatened. Any tool not integrated with daily, oral use in families and communities is likely to remain a symbolic gesture—a morale boost, rather than a true linguistic revival. No technology developed so far can rival the intensive, unsung process of language transmission that takes place between adults and young children, almost always at home.

Response: More dangerously, a magic-bullet mindset might make us misconstrue what language is all about. It has always fallen to people—through extensive and unending efforts of understanding and translation—to sustain the age-old ecology of difference that lies behind the real richness of the world’s information and the world’s relationships. If language is prototypically lived through densely contextualized meanings unfolding spontaneously and dialogically in real time, it is hard to see how any device, however fantastical, could be more than “merely one element in a transient flux of compulsory and disposable products”, as Jonathan Crary puts it. Meaning would be flattened, and what would remain is just metabolism: “how the rhythms, speeds, and formats of accelerated and intensified consumption are reshaping experience and perception,” as Crary writes.


Prompt: Subcultures, speaking chosen dialects, will still flourish online, as they do in cities. But the future of cultures—rooted in history and mostly passed down in families, anchored in distinctive value systems and long-term lifeways—is still up in the air. More deeply different, they are also much more of a threat to any kind of hegemony. This is why we need them, now more than ever. Coding languages are not human languages, which are still an intricate index, after all, of the time that people spend with other people.

Response: Climate is anything but democratic. Agriculture is tied to place. Grapes thrive in the hot winds of Napa Valley while coffee requires the consistent humidity of the equatorial zone. Olive trees are abundant in Spain; cloudberries thrive across the Arctic zone, and nowhere else. Climate change is making climate even less democratic. It amplifies the polarity and raises the stakes. Farmers in Jordan suffer from increasingly saline soils while Alsatian vintners fret over what warmer temperatures mean for their vines. California, subject to alternating periods of drought and flooding, produces over a third of the vegetables and more than half of the fruits and nuts that Americans eat.


Prompt: The need to overcome these environmental challenges has only bolstered our existing, highly globalized food system. States import and export goods in attempt to feed populations and stabilize their own economies. The “food miles” add up; the emissions accumulate. Corporations reap incredible profits in the process.

Response: And nevertheless, global hunger is rising. People are moving into cities at unprecedented rates. Farmers are aging out of the business worldwide. The corporations that control much of the food supply continue to consolidate. Given these pressures, a set of entrepreneurs want to build a climate-agnostic form of agriculture. What if you could grow avocados in Jordan and olives in the Arctic? Deploying the latest developments in automation, machine learning, and computer vision, these high-tech food startups grow indoors, nourishing plants through hydroponic or aeroponic systems and rhythmic exposure to LED light.


Prompt: As is standard in the commercial technology and agricultural industries, the companies don’t disclose their methods. From hardware to plant research to data standards, proprietary and defensible intellectual property forms the backbone of their businesses. But over the past few years, an initiative based at the MIT Media Lab in Cambridge, Massachusetts has started sowing the seeds of a different sort of indoor agricultural revolution—one built on open-source hardware and software.

Response: Agriculture Wants To Be Free The Open Agriculture—or “OpenAg”—ecosystem consists of three parts. The Open Agriculture Foundation protects licenses and manages a community of “#nerdfarmers,” as OpenAg practitioners are called. The Open Agriculture Initiative conducts research and partners with universities—currently Chiba University in Japan and Can Tho University in Vietnam. And Fenome, a startup, is attempting to commercialize OpenAg’s signature product: an open-source “personal food computer.” The project started in earnest in 2015 when Caleb Harper hacked together the first personal food computer with a team of researchers. An architect by training who grew up tinkering with computers, Harper turned to indoor farming following a trip to Japan. There, he saw a plant factory: an immense facility growing tons and tons of greens. Something clicked: the prospect of growing large volumes of produce in a country that imports a significant percentage of its food supply looked like a way to democratize climate.


Prompt: Back home in the United States, several companies were already at work on commercial indoor farms similar to what Harper had seen in Japan. But his proposition was different: he wanted to create a network of semi-automated indoor farms that ran on an open-source platform. The farms would range in scale from small units the size of a mini-fridge to warehouses outfitted with plant towers.

Response: Over the next two years, Harper scaled his experiment into a global community of horticulturalists, mechanical engineers, plant scientists, teachers, and hackers committed to the idea of open agriculture. Like any open-source community, OpenAg enthusiasts swap hardware specs and software code on GitHub, and trade troubleshooting tips on an online forum.


Prompt: But they’re not just hobbyists— they’re crusaders. Instead of highly centralized industrial farms run by Big Ag, they envision a radically decentralized network of customizable farms. They want to give everyone the power to grow anything anywhere. Personal Food Computing The most basic unit of food computing is the personal food computer, now in its 2.1 edition. It consists of a plastic container affixed to a metal frame outfitted with sensors, microcomputers, lights, and electrical wiring. The machine grows plants in a tub similar to what cafes use to collect dirty dishes. A floating styrofoam tray fits snugly inside and has evenly spaced holes where the plants grow.

Response: The tub itself holds water treated with the nutrients and fertilizers best suited to the crop. Some #nerdfarmers outfit their food computers with aeroponics—a system that uses nutrient-dense mist rather than water. An outer shell made of clear acrylic encases the entire box, creating a controlled environment. An array of LEDs affixed to the top of the box delivers light to the plants.


Prompt: Two printed circuit boards form “the brain.” A Raspberry Pi microcomputer hosts the software that allows a farmer/user to control the system from a computer, while an Arduino microcontroller links to the temperature, humidity, pH, and CO2 sensors that collect data about the environment. Let’s say you want to grow arugula from seedlings. You load a “climate recipe” onto the Raspberry Pi that consists of many lines of code. The personal food computer interprets this code, and adjusts the lighting, humidity, temperature, pH, nutrient levels, and CO2 to the appropriate levels: "air_temperature": [ {"start_time": 0, "end_time": 17, "value": 21.1}, {"start_time": 17, "end_time": 24, "value": 15.6}], “water_potential_hydrogen”: [ {"start_time": 0, "end_time": 24, "value": 6}], “air_flush”: [ {"start_time": 0, "end_time": 24, "value": 3}], “nutrient_flora_duo_a”: [ {"start_time": 0, "end_time": 24, "value": 10}], “nutrient_flora_duo_b”: [ {"start_time": 0, "end_time": 24, "value": 10}], “light_intensity_red”: [ {"start_time": 0, "end_time": 17, "value": 1}, {"start_time": 17, "end_time": 24, "value": 0}], “light_intensity_white”: [ {"start_time": 0, "end_time": 17, "value": 1}, {"start_time": 17, "end_time": 24, "value": 0}], “light_intensity_blue”: [ {"start_time": 0, "end_time": 17, "value": 1}, {"start_time": 17, "end_time": 24, "value": 0}] Arugula is only one example. The machine could grow anything, although like the rest of the indoor farming community #nerdfarmers tend to focus on crops with short growth cycles and high value propositions. The less plant you have to grow to get the end product, the more efficient the system. And efficiency is important because the machine isn’t cheap: in all, a personal food computer costs between $3000 and $5000 to build.

Response: The #nerdfarmers I spoke to see personal food computers as a way to bring crop cultivation back to where people live and reduce the distance between farm and table. Everyone I spoke to lives in a city, and by and large, they recognize the current limitations of OpenAg. We aren’t going to see amber fields of grain enclosed in a warehouse-sized “food data center” anytime soon.


Prompt: That’s because the technology is still fairly primitive. Dan Nelson was among the first people to build a personal food computer from the published plans. He works out of AgTech X, a coworking space in Brooklyn for entrepreneurs in agricultural technology. He described the GitHub instructions as “terrible.” And he isn’t much more optimistic about the machine’s latest version: “I think it’s totally inaccessible and totally unaffordable for the overwhelming majority of people who aren’t doing it in a research context,” he said.

Response: It’s inefficient to grow anything except leafy greens. The economics are wildly bad. Still, #nerdfarmers see the personal food computer as an important first step: not a wholesale solution to a broken food system, but a useful teaching tool, and a modest opening salvo in the battle to wrest control of agriculture from Big Ag.


Prompt: Through the forum, Nelson met Peter Webb, who lives in St. Louis. Webb almost discovered the OpenAg community too late. He wanted to build a machine that grew food. So he scoured the internet for guidelines, parts, and advice. His research took him into the depths of the hydroponic cannabis industry: most of the material he found online focused on equipment for growing weed. The parts were priced accordingly—up to $1000 for LED lights, several hundred dollars for grow tents, $100 for reservoirs.

Response: Spending thousands of dollars to grow weed might be worth it. But spending that amount of money to grow a tomato plant, as Webb put it, is simply not sustainable. Working from his bedroom, without institutional support or corporate research dollars, he put months into creating a prototype for an open-source build-your-own-hydroponic-farm kit without any knowledge of the MIT project. He listed ten of them for sale on eBay, but none sold.


Prompt: Not long after, Webb found out about OpenAg, and he burst into tears. “Within two months I went from being totally alone, a crazy kid in a closet to having 1000 people on the forum, to having a community of people.” Webb connected with Harper, and he now leads a team that is developing designs for a cheaper, leaner food computer. The current prototype costs only about $300 to build, and uses lower-cost materials: mylar and PVC piping instead of metal frames and sheets of acrylic. Moreover, it’s simpler. The personal food computer has 152 unique parts; Webb’s version has thirty-one. This also means fewer aspects of the machine are automated, which means more work for the user.

Response: Big Data Farming You need efficient hardware to grow food indoors, but both #nerdfarmers and agtech entrepreneurs recognize that the true value lies in the software and the data. “No company is going to get millions of dollars for anything other than proprietary algorithms and data,” Webb said. But the indoor farming industry is still young—by and large, it lacks standards, said Allison Kopf, the founder of Agrilyst, a software company for indoor growers. The pressure that venture capital investors put on startups to protect their intellectual property is counterproductive, she thinks. “It would be hugely beneficial to the industry as a whole if everyone could sit down and say how do we develop these standards from climate devices to software.” From hardware to software, the industry needs a playbook, and OpenAg could help build that. The open-source community could develop a common set of standards, practices, and source code that startups might use to build their businesses.


Prompt: One example is climate recipes, the source code of OpenAg. In the past year, the MIT team has honed in on how to write these recipes, but the community hasn’t seen much of that research yet. Harper has posted about partnerships with AI firms and doubled down on phenotypic research—which involves learning how to put environmental stress on basil plants in order to produce particular traits. But the dream of the Open Phenome project—a public database full of climate recipes that anyone can download onto their personal food computer—is some ways off.

Response: Data is another weak point. The data that personal food computers collect, Webb said, doesn’t easily translate into meaningful plant science metrics. It’s fine to measure CO2 levels, temperature, and humidity, but without a clear understanding of how those influence the biomass of plants, the information isn’t all that valuable. A climate recipe should ultimately clarify the details of output and yield. “There is this disconnect between open field plant science and this controlled environment agriculture,” Webb said.


Prompt: He often thinks about Monsanto, where his dad worked: although anything but open-source, the company has mastered the art of data collection. Its extensive laboratory research on corn and soy has given it an enormous competitive advantage. For digitized agriculture to fulfill its potential, it has to follow Monsanto’s lead on data. It must standardize how it gathers information, and develop a process for converting that information into knowledge that improves the growing process. If and when OpenAg hacks its way to a platform where users can post and exchange climate recipes, the open-source model could prove a valuable tool for commercial ventures—much as Linux, Apache, and countless other projects have for the tech industry.

Response: Open-Source Capitalism For all its idealism about disrupting Big Ag, OpenAg may ultimately help strengthen it—or reproduce it in a different form. Just because open-source projects give their source code away for free doesn’t mean they aren’t immensely useful for commercial enterprises. Today’s tech giants rely heavily on open-source projects.


Prompt: The question, then, isn’t whether OpenAg will benefit business, but which businesses will benefit. Will traditional Big Ag companies like Monsanto embrace OpenAg research and development? Or is it more likely that we’ll see tech companies move into the space? It’s too soon to know, but the signs seem to point towards tech. Already, the indoor farming industry has modeled itself more after tech companies than after agricultural ones. SoftBank’s Vision Fund—the world’s largest corporate venture fund—made the single biggest investment in agtech on record in Plenty, a vertical farming company based in South San Francisco. Jeff Bezos’s personal venture fund also invested in Plenty—a significant gesture, perhaps, given Amazon’s recent acquisition of Whole Foods.

Response: It’s possible to picture OpenAg becoming big business in the near future. Imagine an Amazon warehouse that not only stocked toilet paper and televisions, but grew broccoli and lettuce—and sent you any of the above in the mail. “A suite of production technologies can simultaneously embody radical ideals of cooperation, freedom, and social change and be an ever more widely embraced model of capitalist software production,” write Samir Chopra and Scott Dexter in their paper, “The Political Economy of Open Source Software.” The same is true of OpenAg: developing shared networks of agricultural knowledge can embody utopian ideals even if it serves to retrench industry.


Prompt: When farmers, like software developers, have the right to modify and alter their code, everyone benefits. The #nerdfarmers vision of a radically decentralized digital food system isn’t likely to prevail over the deeper pockets of the closed-source community, but the work still matters. Linux is better than Windows, and farmers have long fought to preserve their rights to “open-source” methods from sharing seeds to cultivating common lands. As we race towards a digital future of food, the ability to freely adopt and adapt technology amounts to a small yet fertile plot for resistance.

Response: 1. Ever tried. Ever failed. Fail again. Fail better. Growing old in Paris, the Irish writer Samuel Beckett could never have expected this fragment to become a tech industry mantra. But it has, and with good reason: The success of Silicon Valley is built on failure. This may be its greatest innovation. The region broke ahead of its rivals because it encouraged people to take big risks and did not punish them when those risks did not pay off.


Prompt: At one level, this forgiving attitude reflected the influence of Bay Area hippies who shaped tech culture from the beginning. At another, it reflected the rise of new funding models. Early venture capitalists embraced risk rather than avoiding it; they expected most of the companies they invested in to fail. The failures did not matter, so long as the few that did succeed succeeded big.

Response: The rise of software and, later, the web, made it possible to succeed at a whole new scale. This in turn enabled investors to tolerate more and bigger losses, because when the moonshot landed, the returns were astronomical. When you found the right product-market fit, you could eat the world, fast. In the meantime, with companies appearing and disappearing so quickly, people had an interest in staying friends, in sharing expertise, in going to Burning Man together.


Prompt: Not everyone gets invited to the caravan, of course. To get funded, it still helps to be white, to be cis, to be male, to have been admitted to Stanford, and even completed a course or two, before dropping out to raise your Series A. While some people can only fail up, others fail out, and many never get a chance to try at all. It also helps to be young—or, as the saying goes, to “have a lot of runway.” Don’t trust anyone over thirty used to be a political slogan. Now it’s a business strategy.

Response: Above all, to fail well, you must undertake a certain type of endeavor: A startup that could scale massively. A sandwich shop that fails was a bad idea. A sandwich delivery app that fails was a valuable learning experience for its founders; the market timing just wasn’t quite right. Struggling taxi companies are in terminal decline, but a ride-hailing app that loses two billion dollars per year is one of the most successful companies in the world.


Prompt: This model of success may seem strange, when seen from Mars. But for the people to whom it is available, tolerance for failure is one of the best qualities of Silicon Valley. Letting people try things that might not work really does stimulate creativity. It fosters a respect for hard problems, and collaborating on those problems creates a sense of community. The course of true innovation never did run smooth.

Response: 2. If Silicon Valley is built on failure, it is also built on stories about failure. Once upon a time, a Reed College dropout got fired from the company he founded, then came back more than a decade later to invent the iPod and iPhone. A UCLA dropout started a company that declared bankruptcy to avoid lawsuits, started another company where he committed tax fraud, moved in with his parents to save money, moved to Thailand to save money, then came back and cofounded Uber.


Prompt: Travis Kalanick told this story at FailCon, a one-day conference started in 2009 in San Francisco to teach startup founders to “learn from and prepare for failure, so they can iterate and grow fast.” Since then, it has become so common for entrepreneurs to talk about their failures in public and in long Medium posts that FailCon itself started failing to attract the audience it once did. So the founder has licensed the brand in “over a dozen cities on six continents.” You can attend FailCon in Ulaanbaatar. You can attend FailCon in Porto Alegre.

Response: You go to learn the conventions of a genre. There are many ways to fail: go bankrupt, get acquired, get acqui-hired. To fail well, however, you have to get your story straight. A good founder story always describes failure without bitterness. It has been an incredible journey. I learned a ton. It presents every failure as temporary—a waystation to success.


Prompt: Storytelling is not only for founders. It is a skill that everyone at a startup should cultivate, because the vast majority of startups will fail. And when you suddenly learn that you are out of work and all that equity you pulled all those all-nighters to earn is worthless, no job interviewer will want to hear that you got screwed over. They want to hear how you grew.

Response: Even if you do get hired at one of the biggest tech companies, the risk of failure does not go away. On the contrary, a funny thing happens: the more successful the company becomes, the higher the bar for any internal project to be considered successful. If you build a startup that gets ten thousand users, you could sell that startup for seven figures. But once you work at a platform with billions of users, if you design a feature that only a few million use, your bosses will shake their heads and shut it down.


Prompt: If something you spent years working on fails, you do not just need a story to tell your current or future employers; you need a story to tell yourself. You want to be able see those years as part of an incredible journey, too. 3. Silicon Valley tells itself one set of stories about failure. It has another story that it tells everyone else. Namely, that the old world has failed. The old world was analog: government bureaucrats, boring businesses, factory jobs, gray flannel suits. The new world is flexible, Technicolor, gymnastic, young. It has no dress code. It is neither white-collar nor blue-collar: it wears no collars at all.

Response: In the new world, everyone will live a better life. This was the promise sold for decades by tech industry leaders and politicians alike: tech would drive American growth, creating widespread prosperity. This sounded good in an era of deindustrialization and spiraling inequality. It sounded even better after the 2008 financial crisis wiped out middle-class savings. As wages continued to flatline, the smartphone-driven gig economy would pick up the slack. Asia might be rising, but Silicon Valley still provided a bulwark of essentially American creativity; just look at all those Chinese nouveaux riches standing in line to buy the latest iPhone.


Prompt: A set of political assumptions followed from these claims. The most important of these was that it was all right for a handful of former failures to accumulate vast amounts of money and power, so long as what was good for Silicon Valley was good for humanity. If tech was the future, why would anyone, or any regulation, want to stand in its way? The government bought this; the media bought it; almost everyone seemed to like two-day delivery and the ability to chat and share pictures for free. It was not just in America. The Silicon Valley model was replicated all over their world. Yet, in the past year, the narrative has started to change.

Response: Anyone paying attention knew that there were problems in the tech industry, and with its products. We knew about the systemic racism and sexism, the exploited gig workers and Silicon Valley service staff, the bots and the trolls and the fake news, the swarms of Pepes and incels. Now, since revelations about the role of disinformation on Facebook, Google, and Twitter during the 2016 election, criticism of Big Tech has reached a new and unprecedented level.


Prompt: The Silicon Valley story has come down to earth. Maybe these companies are just companies after all. Maybe they are pursuing their self-interest as any capitalist firm would. Maybe, even if their IPOs occasionally toss some money to California state coffers shortchanged by Prop 13 (o hai, Snap!) the interests of tech companies do not perfectly align with the interests of humanity.

Response: There are signs that the interests of tech leaders aren’t even perfectly aligned with those of most tech workers. After the 2016 election, many good people who entered the industry because they wanted to change the world were appalled to see their supposedly liberal bosses warming up to Trump. More recently, a wave of worker organizing within Google, Microsoft, and Amazon against collaboration with the Pentagon and ICE shows how big the rift could grow.


Prompt: Burning Man could get awkward. 4. As scandal after scandal surfaces, Big Tech CEOs apologize for their mistakes. We will do better. Our AI will fix it. For many onlookers watching Mark Zuckerberg before Congress, (“We sell ads, Senator”) a troubling question began to emerge: What if the mechanisms that enabled advertisers to reach “Jew haters,” white supremacists to harass BLM activists, or far-right conspiracists to flood users’ feeds with disinformation were not, in fact, mistakes? What if Facebook is doing what it was designed to do? The system may be working exactly as it is supposed to. If so, is the definition of success we have been using wrong? What if the success of Silicon Valley is failing most of us? This issue ventures a few possible answers to these questions. It explores what happens when technology blows up and breaks down. It looks at failures of code, institutions, narratives, and imagination. In so doing, it tries to imagine a better way to live with our machines—and to build new ones.

Response: There is no shame in failing, until there is. Having been exempt from scrutiny for so long, many tech leaders seem genuinely stung by their first taste of criticism. Don’t they see? We’re working so hard. Taking a cue from Trump, some of them have take to Twitter to denounce their critics as liars, and to challenge the legitimacy of the media as a whole.


Prompt: There has long been the sense in Silicon Valley that anyone who criticizes is simply a hater. We take the opposite view. Engaging with failure is, as FailCon could have taught us, the first step to growth. Only, that growth may have to happen in a different direction than Silicon Valley has been imagining. We may not need to iterate to grow faster but act boldly to create a more expansive and equitable world.

Response: The only failure that should frighten us is not taking advantage of the opportunity that this moment presents. Complacency is no way to honor the disruptive potential of technology. To paraphrase one interview in the following pages: Criticism can be the highest form of love. I am on the phone with the one-time owner of Kozmo.com. Back in 2001, Kozmo.com was going to deliver your Starbucks coffee in less than an hour. Its former owner is… not what you’d expect.


Prompt: Martin Pichinson is about seventy, a former music manager who came to Silicon Valley in the mid-1980s. His business partner is Michael Maidy, another septuagenarian who, judging from a Google search, favors dark suits that look about a half-size too big for him. Maidy was recently the CEO of another failed tech company: Pebble Tech, LLC, maker of smartwatches. Pichinson and Maidy look about as far from our image of the Silicon Valley CEO as you can imagine. But they are nevertheless an important, if rarely glimpsed, part of its ecosystem.

Response: Their actual company is Sherwood Partners, and unlike Kozmo.com, Pebble, and about a thousand other companies they have wound down over the years, it (a) still exists and (b) its business is always booming. The company is Silicon Valley’s premier specialist in “assignment for the benefit of creditors” (ABC)—a process by which insolvent companies assign their assets, titles, and property to a trustee.


Prompt: ABC was how Pebble Tech, LLC came into existence: it was, for its brief life, simply a collection of Pebble’s remaining assets, to be distributed among various creditors, employees, and shareholders.  This was also how Maidy briefly became the figurehead of a zombie version of the once-hip startup. When you’re dealing with Sherwood, things are going badly. “People don’t like to talk to us, because they think, ‘If I’m talking to Sherwood, it’s a sign I’m in trouble,’” Pichinson says. Maidy’s and Pichinson’s names are all over public filings. While many of the lawyers and VCs I spoke to for this story try to stay out of the headlines, Sherwood doesn’t have that option. TechCrunch once called Pichinson “the Terminator of startups,” and many journalists on the Silicon Valley beat seem to check in with him periodically to see how business is going—if he’s upbeat, it’s time for another culling of a herd.

Response: They’re not undertakers, Pichinson insists, though he too can refer to ABCs as “a private funeral.” Silicon Valley’s failure industry runs on discretion and convenient amnesia. Sherwood Partners is a place of memory and a place of failure. “I am the guy who closed down a lot of the high-flying dot-coms,” Pichinson notes, not without a note of pride. Receiverships, bankruptcy, ABC—Sherwood is like a one-stop-shop for whatever the opposite of the image Silicon Valley likes to project is. And it has been for almost thirty years.


Prompt: Fail Better Silicon Valley thinks it has failure figured out. Even beyond the clichéd embrace of “failing better,” a tolerance for things not going quite right is baked into the tech industry. People take jobs and lose them, and go on to a new job. People create products that no one likes, and go on to create another product. People back companies that get investigated by the SEC, and go on to back other companies. They can even lie on behalf of a company like Theranos without any taint whatsoever. In Silicon Valley, it seems, there is no such thing as negative experience.

Response: The attorneys and consultants who have grown old with the industry’s failures, from Pets.com to Pebble, are anything but harsh in assessing their “clients.” “They are not bad,” one old hand insists. Instead, “the question really becomes: how many new ideas can society handle?” Even Sherwood Partners doesn’t see themselves as a repository of Silicon Valley’s fuck-ups. To them it’s about luck, bad timing, the wrong blend of personalities. “They didn’t fail, they just didn’t come in first.” That can be deeply charming: rather than make failure, messiness, and growth something to hide, the ethos of the tech industry puts fallibility and vulnerability at the center of life. The guys at Sherwood have some of that relaxed California vibe, plus a dose of paternalism—they wind down companies started by people less than half their age. They try to make it a teachable moment and move on.


Prompt: At the same time, Silicon Valley’s tolerance for failure has long sustained an obsession with youth. If a founder fails, tech discourse interprets it as a sign of young vigor. In a country in which twenty-five-year-old white rapists are “still boys” and black twelve year-olds on the playground “look like adults,” the question of who gets to be a kid and who counts as a grown-up is clearly charged with privilege.

Response: In 2017, a chastened Travis Kalanick admitted, “I must fundamentally change as a leader and grow up.” Even in a place as choc-a-bloc with balding skateboarders and middle-aged trick-or-treaters as San Francisco, a forty-year-old CEO of a $15-billion company casting himself as an overenthusiastic kid who just needs to get his shit together is a bit much.


Prompt: Failing in Silicon Valley is often a prerogative of the young—or, in Kalanick’s case, the adolescent-acting. And people don’t talk about how much less sustainable it has become to be young in the Valley. One VC who back in the early aughts grew a tiny startup into an $80 million company with more than 250 employees reminisced to me about the early days when “we just lived with our parents in Toronto.” “Our labor force was ourselves and we paid for the servers by credit card,” he continued. Then he reflected a moment. “That’s no longer possible, which I guess is what makes us necessary.” Another change he has noticed: a lot of big funds have moved towards investing earlier in the life of a company. Where once a founder may have come to them looking for a Series A round, now they are coming for angel funding. This means that any falling out of favor with investors will be extremely public—“If Sequoia offered you funding and suddenly isn’t around for the next round, I ask myself: what do they know that I don’t?” Silicon Valley’s tolerance for failure is partly predicated on a privacy that is starting to dissipate.

Response: But the thing about failing is that it seems to carry opposite meanings depending on who does it. If a traditional brick-and-mortar business hemorrhages money as unregulated digital competition moves in, then that’s just a sign that brick-and-mortar deserves to die. By contrast, if a disruptive New Economy startup loses money by the billions, it’s a sign of how revolutionary and bold they are.


Prompt: There is an entire cottage industry in Silicon Valley devoted to making this distinction. The fawning court press, the hype machine, the angel investors are always ready to explain why a venture that has all the hallmarks of a total failure is actually a genius idea. And those aren’t the only businesses built on the reality behind “fail better.” There’s also the handyman at an incubator who lets all the denizens pick over the carcass of any startup in the building that has gone belly-up: swivel chairs, ping-pong tables, swag and lots of Soylent. There are lawyers busy disentangling the Gordian knot tied by youthful idealism. And there are companies like Sherwood, which step in and take over your company when all hope of success has faded.

Response: Daddy’s Home The clean-up crew stays deliberately out of sight. “It’s in bad times that they hear about us,” Pichinson says, and he sounds regretful about it. The careers being made in Silicon Valley have something magical about them, and perhaps for that reason all of the professionals working behind the scenes get the sense that their clients think consulting them will constitute a capitulation. An admission that what they’re running is a business, that their career is in the end just a career, that gravity has some kind of purchase on their meteoric trajectories.


Prompt: A lot of the younger VCs are themselves highly successful founders, and the contingency of their own success hasn’t yet sunk in. I’ve generally found it to be the case that people who strike it rich in Silicon Valley are in a way dumbstruck by their own success. It comes so early, so unpredictably, so noiselessly—a bolt of lightning out of a blue sky. But people have to make sense of what happens to them. Some withdraw almost shyly from their own good fortune. But those who don’t have to tell themselves stories about it. Why do they deserve their good fortune? What does it mean? This matters because only those who have encountered the most stupefying, most inexplicable success will end up funding the next generation of startups. If you wind down, get acqui-hired, or make some other type of “graceful exit,” you usually don’t have the cash to do venture capital investing. Among those who do have that kind of cash, their sense of reality can be deeply warped—perhaps it almost has to be a little bit, shell-shocked as they all can be by their extraordinary luck.

Response: That might be why a nimbus of unreality envelops the early stages of funding. At the center of this colossal churn of money is a kind of stunned muteness. The investors all tell me: I wish founders spoke to me sooner. “We’re not smarter, but we’ve seen more pain in the industry than anyone,” Pichinson says. It makes sense why kids who have glided almost without resistance into Stanford, alumni meet-ups, and VC offices may not want to be reminded of pain. Many of them, Pichinson points out, have never experienced a tech recession—they were in kindergarten, if that, when the dot-com bubble burst. As one lawyer I spoke to explains, “Often enough, the only authorities they run into outside of their age cohort are venture capitalists, who are either very young themselves” or are busy speed-dating a bunch of startups, without providing much mentorship in the process.


Prompt: But reality may not be worth much. In the league in which these startups play, one founder tells me, reality may seem like a poor guide to business. The crucial moment in the life cycle of your business arrives at a moment when you’re “expecting to get millions of dollars for a PowerPoint deck.” You have to convince a room of investors that your company could make hundreds of millions of dollars—“otherwise why not just get a business loan?” The term “lifestyle business,” he points out—meaning a business that is designed to keep its owners fed and happy rather than making them brain-meltingly wealthy—is “kind of a pejorative in this world.” Because to be reminded of why people normally work is an unwelcome dose of real life, of gravity, of adulthood.

Response: This is where the anonymous lawyer I spoke to sees his niche: hard truths, a little bit of adult sensibility. He runs his business as a tiny two-man firm with a boutiquey reputation. He doesn’t advertise his services, and people come to him only by referral. A Bay Area local—Berkeley-bred, Berkeley-educated, Bohemian Club—he has seen tech come in waves. He’s gotten older, while tech has gotten younger. Many of his clients are straight out of college, if that. They’ve imbibed an ethos of egalitarianism and a vocabulary of aspiration. This forces him to function as reality principle, as mentor, and, often enough, as a Cassandra.


Prompt: He also functions as an avatar of a centuries-old corporate structure inimical to their visions—hierarchical, vertical, based on more than just personal relationships. He’s only at the end of his thirties himself, but surrounded by so much immaturity he’s started to sound middle- aged by comparison. “It’s weird for me to think of myself as the adult in the room,” he says.

Response: By the time they come to him, many startups have some legal framework in place. Which can be a good thing or a bad thing. He sees terms between a young company and its VC investors, where both sides are represented by the same law firm—the investors’. That’s not technically a conflict of interest but, he says, it’s a massively bad idea.


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

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


Prompt: Although most of Sherwood’s work is with investors, employees, and vendors, they also hold a massive database of patents amassed from their assignees. “We probably monetize more patents than anyone else in the world,” Pichinson says. And he’s not wrong: Agency IP, Sherwood’s sister company, is nominally a consultancy, but in fact spends most of its time actively exploring the applicability of patents left behind by the companies Sherwood has buried. Like what William Morris agency does for screenplays, says Pichinson, who now operates from LA’s “Silicon Beach.” He makes it sound glamorous.

Response: You Can’t Get Rid of Wealth The guardian angels of better failure in Silicon Valley are the investors. When men like Pichinson are pretty Zen about failure, it makes sense—after all, it’s their business. When lawyers who charge by the hour seem okay with failure, then sure, why not, they get paid one way or the other. But what about the investors who sink money in ventures and either get some of it back or none of it back? It’s easy to assume that the shrug with which they treat every flop is a facade. It’s unnerving to realize that it’s absolutely not—and for good reason.


Prompt: The reason is what one VC calls “the repeat business effect.” Sure, a twenty-four-year-old can run his company into the ground—but he’s still a twenty-four-year-old, with time and energy for another startup, and then another. And any one of those could pan out and make everybody fantastically rich. It is, as one founder told me, “the luxury of having a lot of runway left.” Why would you upset a person like that and potentially miss out on a future payday? There is a lot of money sloshing around Silicon Valley in search of that payday. It laps up Sand Hill Road, all the way to the famous Rosewood Hotel with its Tesla-filled parking lot and tech divorcees on the prowl. There’s the old Chris Rock joke about the distinction between being rich and being wealthy: “You can’t get rid of wealth,” Rock says. Watching the well-preserved faces at the Rosewood bar, you believe it. The money that pours in—from pension funds, hedge funds, private investors—has to go somewhere. It is agnostic about individual failure or success; its mantra is the law of averages. By the time one venture crashes and burns, everyone is already on to their next one.

Response: But failure comes encased in bubble wrap—at least among those who have a reasonable expectation of running into each other again. What about those who don’t? Many of the employees who have foregone sleep, pay, healthcare, and a social life for the benefit of now-worthless shares will not be instrumental in making the next spin of the wheel the winning one.


Prompt: There are many ways to close up shop in Silicon Valley: get acquired or acqui-hired, wind the company down, buy out your investors and start anew as a small business. Depending on how a company dies, however, most or all of the employees will not be part of these transactions. Google won’t acqui-hire the receptionist, or even the publicity person. They won’t take on those who were only contractors, or those who mysteriously got the boot right before a desperate final funding round.

Response: And even among those with titles, salary, and equity, the acqui-hiring party gets to pick and choose: in an acqui-hire truly deserving of the name, the company’s product and assets matter little. It’s really a way of hiring a very small group of people—and it falls to that group to stand up for those members of the company that the hiring party is not interested in. “They know what they’re prepared to spend,” one person whose company got absorbed into Google told me. “How equitably that gets spread around is basically one big prisoner’s dilemma.” Given the gender dynamics of Silicon Valley, that means that men usually fail better. Given that many of the founders meet in college, it means that having gone to university with the top team is a plus. Those excluded are people who are treated as contractors and received only equity, people who vested and then left, people who have been thrown out before they reach a vesting cliff after a mysterious performance review.


Prompt: And for them, the law of repeat business reveals its ugly side. “None of this litigation happens in this industry, because nobody wants to be blackballed,” my anonymous lawyer says. Or, as an angel investor puts it, it’s important that even a failed venture “facilitates the founder’s story.” Something similar seems to be true for employees: “I learned a lot” is a story that whoever is hiring, seeding, funding, or advising you on your next undertaking is going to want to hear. “The bastards screwed me out of a bunch of money” isn’t.

Response: That’s the funny part of the tech industry’s narrative about itself. For tech, failure is always assumed to be temporary; for everyone else, it’s terminal. Taxicab companies are going out of business because they’re losing money? Creative destruction, my friend—sink or swim. Uber hemorrhages cash? Well, that’s just a sign of how visionary the company is. This double standard justifies the exploitation of workers outside of the tech industry—and, in certain cases, the exploitation of workers within it.


Prompt: If the incredible rise of computing is one of the biggest stories of the twentieth century, then the failure of the nation that invented the electronic computer to capitalize on it is undoubtedly one of history’s most important cautionary tales. In 1944, Britain led the world in electronic computing. The top-secret codebreaking computers that the British deployed at Bletchley Park worked round the clock to ensure the success of D-Day, and the Allies’ win in Europe. At a time when the best electronic computing technology in the United States was still only in the testing phase, British computers literally changed the world.

Response: After the war, British computing breakthroughs continued, and British computers seemed poised to succeed across the board, competing with US technology on a global scale. But by the 1970s, a mere thirty years later, the country’s computing industry was all but dead. What happened? The traditional history of computing would have you understand this change through the biographies of great men, and the machines they designed. It would gesture towards corporations’ grand global strategies, and the marketing that those companies pushed to try to define what computers were for an entire generation of workers. It would not, however, focus on the workers themselves. And by ignoring them, it would miss the reasons for this catastrophic failure—a failure that remains a cautionary tale for many other countries today, particularly the United States.


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

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


Prompt: The real-life Anne shared many of the same talents and characteristics as the fictional one. She was also young, white, and had technical skills. The fictional Anne enjoys an exciting career in a growing field, as she proves her knack for technical work, laboring with quiet diligence each day on a secret, high-tech airplane project. She eventually shows up her male superiors by figuring out a critical engineering error that was holding up the project. While they flounder, trying desperately to figure out the flaw in their design, she hesitantly points it out, afraid of embarrassing them. In the process, she not only saves the project but she wins the heart of the male coworker she had her eye on—and allows him to take credit for her breakthrough.

Response: As women, neither the real Anne nor the imaginary one was making an unusual decision to put their career behind their other life goals. In fact, each was making the socially expected, and strongly encouraged, choice. In the book, Anne stays in the workforce after marriage, but only after being admonished that she must put her husband’s career first. Even in fantasies, women weren’t allowed to think that their careers could come first—the best Anne might do was to try to juggle her work, her family, and her husband’s needs, knowing that if a ball needed to drop, it would be her career.


Prompt: A young woman named Anne Davis wears a punch tape dress at her “retirement party” as she leaves her job to get married. In the real world, this juggling act was difficult, and often impossible. Middle-class women who had the mettle and the privilege to try it encountered major obstacles. And the fact that it was still seen as completely inappropriate for women to hold authority over men in the workplace meant that women who struggled to stay in their jobs rarely got very far. So the real-life Anne celebrated her retirement with half a dozen other young women computer workers—most of whom would go down the same path in the next few years.

Response: Collective Failures When we talk about computing history, we rarely talk about failure. Narratives of technological progress are so deeply ingrained into our ways of seeing, understanding, and describing high tech that to focus on failure seems to miss the point. If technology is about progress, then what is the point of focusing on failure? Up until recently, we also rarely talked about women in relation to computing.


Prompt: The first silence is related to the second. Women, after all, were seen as having largely failed in computing until recent historians’ attempts to correct that assumption. But as it turns out, technological failure and womens’ erasure are intimately related in more than one way. When we put these facts together—our avoidance of failure, our ignoring of women in computing, and our tendency to see women’s contributions as less important—curious patterns start to emerge.

Response: The failure of one unnamed and ignored postwar computer worker is a good place to start. In 1959, this particular computer programmer faced a very hectic year. She needed to program, operate, and test all of the computers in a major government computing center that was doing critical work. These computers didn’t just crunch numbers or automate low-level office work—they allowed the government to fulfill its duties to British citizens. Computers were beginning to control public utilities like electricity, and the massive and growing welfare state, which included the National Health Service, required complex, calculation-dense taxation systems. Though the welfare state was created by policy, it was technology that allowed it to function.


Prompt: In addition to doing all of her normal work, our programmer also had to train two new hires. These new hires didn’t have any of the required technical skills. But once she trained them, which took about a year, they stepped up into management roles. Their trainer, meanwhile, was demoted into an assistantship below them. She succeeded at her job, only to fail in her career.

Response: That the trainer was a woman, and that her trainees were both young men, was no coincidence. Nor was it coincidental that, as a woman, she had the technical skills for a job like this while they did not. That’s because before computing became electronic, women were seen as ideal for what was considered mundane calculation work. Though this work often required advanced mathematics knowledge, it was perceived as unintellectual. Before a computer was a machine, it was a job classification—these women workers were literally called “computers.” Even when electromechanical and then electronic computers came in, women continued to do computing work. They programmed, operated, troubleshooted, tested, and even assembled these new machines. In fact, IBM UK measured the manufacturing of computers in “girl hours” (which were less expensive than “man hours”) because the people who built the machines were nearly all women. Meanwhile, the British government, the largest computer user in the nation, called their computer workers the “machine grades” and later, the “excluded grades”—excluded from equal pay measures brought into the Civil Service in the 1950s. Because their work was so feminized, the government declined to give them equal pay and raise their pay to the men’s rate on the basis that the men’s wage was almost never used. Therefore, the lower, women’s wage became the default market rate for the work. So concentrated in machine work were women that the majority of women working in government did not gain equal pay.


Prompt: By the mid-to-late 1960s, however, the low value assigned to computing work was starting to change. Not because the work itself was changing, but because the perception of the work was. Instead of being seen as intimidating behemoths that were only good for highly technical tasks, computers were now becoming widely integrated into government and industry. Their great power and potential was growing more apparent. Suddenly, low-status women workers were no longer seen as appropriate for this type of work—even though they had the technical skills to do the jobs.

Response: So the UK faced a major problem: all of the workers who could do this work were no longer the type of workers that management wanted doing the work. Instead, managers wanted people who would eventually become managers themselves to control these newly important machines and all of the decision-making that was being programmed into them. That excluded women. In this era, women were not supposed to be in positions of power over men. Both implicit and explicit prohibitions prevented women from managing men or mixed-gender workforces.


Prompt: Moving Out, Moving Up Around the same time that the woman programmer trained two men to replace her, a young woman named Stephanie Shirley embarked on a technical career at the prestigious Dollis Hill research station—the same government agency where the Colossus codebreaking computers had been created during World War II. Shirley had been a child during the war, born in Germany, and she was Jewish. She was evacuated out of Nazi-occupied Europe with 10,000 other children on the Kindertransport, a humanitarian refugee program designed to take Jewish children to England. By comparison, the United States allowed in little more than 1,000 children through a similar program.

Response: Grateful for the chances afforded by her adoptive country, Shirley set out to make the most of them. Yet early in her career she began to chafe at the confines of British culture. With a degree in math, a good work record, and a master’s degree on the way, Shirley was the perfect candidate for promotion—or so she thought. As she was denied promotion after promotion, she started to understand that her role was being defined by things other than her technical skill and education—that the much-vaunted “meritocracy” of the government service was anything but. “What shocked me was the discovery that, the more I became recognized as a serious young woman who was aiming high—whose long-term aspirations went beyond a merely subservient role—the more violently I was resented and the more implacably I was kept in my place,” she wrote in her memoir.


Prompt: After being denied another promotion, one that she’d earned several times over, she eventually learned that the men evaluating her were resigning from the promotions board rather than making a decision on her case. “They disapproved on principle of women holding managerial posts,” she found out, so they would rather resign than consider her for a promotion. “I was devastated by this: it felt like a very personal rejection,” she recalled.

Response: After hitting the glass ceiling first in government and then in industry, Shirley did what women were supposed to do—what the two Annes and so many other women had been encouraged to do—she got married and resigned from her position. But, unlike the Annes, she wasn’t happy about it. She still had the skills, the intelligence, and the drive to work in computing, and she knew many other women who were in the same situation—being stymied in their careers not because they weren’t good enough, but because they were women.


Prompt: Because she saw the need for computers growing, she knew that people who could program would be essential. Only they could figure out how to unlock the potential of the new mainframes that so few managers understood, even as those managers earmarked hundreds of thousands of pounds to buy them. So although Shirley got married and started a family, she continued to work. In 1962, she started her own software company, Freelance Programmers, out of her home. When she had stationary made for her new company she half-jokingly put the name all in lowercase, because “we had no capital at all.” Nevertheless, she began to recruit women who had similarly been forced into “early retirement” by having children or getting married. Once her business began to grow, she published an ad seeking people for full-time programmer positions, in the classified section of the Times of London. It read: “Wonderful chance, but hopeless for anti-feminists.” In other words, the company had a woman boss. The ad also announced that there were “opportunities for retired programmers (female) to work from home.” In 1964, this was revolutionary.

Response: Shirley’s freelance programmers worked from home in an era when computer time was so expensive that most programming was done on paper before being punched onto cards and then tested on an actual machine. Programming from home was therefore not a problem, as long as you had a telephone to collaborate with your co-workers. Indeed, one of her programmers was once chastised by a company for using too much computer time to debug a program. Programming without a computer was cheaper—and preferred.


Prompt: Programming from home also allowed women to simultaneously take care of their young children and fulfill their domestic responsibilities. To make things seem more professional, Shirley played a tape recording of typewriter sounds in the background when she answered the phone at her house, in order to drown out the sounds her young son might make. And when she was unable to get contracts early on, she took her husband’s suggestion that she start signing her letters with her nickname instead: “Steve.” With Steve Shirley as the public face of the company, business began to take off.

Response: The Real Ann(e) in Electronics Like many startup founders, Shirley was meeting a consumer need that was still barely understood. In the early 1960s, most software came packaged with the computer itself or was written in-house after a company purchased a mainframe. Software was not considered a product in its own right—and few people expected that customers would actually pay for it separately after spending so much money on a computer.


Prompt: Shirley realized that they would. She had seen the need in both government and industry for programmers who could unleash the potential of expensive hardware with good software. Without software, after all, computers didn’t do anything, and with poor software they couldn’t fulfill their potential or justify their cost. Shirley also knew that British industry and government were getting rid of most of the people who had programming skills and training because they were women, thereby starving the entire country of the critical labor that it needed to modernize effectively.

Response: Shirley scooped up this talent pool by giving women a chance to fulfill their potential. Offering flexible, family-friendly working hours and the ability to work from home, her business tapped into a deep well of discarded expertise. Because people who could do this work were an absolute necessity, the government and major British companies hired her and her growing team of women programmers to do mission-critical computer programming for projects ranging from payroll and accounting to cutting-edge projects like programming the “black box” flight recorder for the first commercial supersonic jet in the world: the Concorde.


Prompt: A woman named Ann Moffatt led the Concorde programming team. And unlike the fictional Anne from Anne in Electronics, she kept the credit for her work. Working from home, Moffatt managed a team of women who also worked from their homes. In fact, this was the first time that Freelance Programmers had undertaken a project managed and staffed exclusively by remote workers—rather than being overseen by one of the four full-time managers who operated out of the small office space Shirley had rented a few years after starting the company out of her house. The arrangement of using remote workers to manage projects worked so well that Ann would go on to become technical director at the company, in charge of more than 300 home-based programmers.

Response: Moffatt sits at her kitchen table in 1966, writing the code for the Concorde, while her baby looks on. Much like Stephanie Shirley, Ann had begun working in technical roles in the 1950s, but had encountered a roadblock once she had children. The feminist business practices of Freelance Programmers let Ann continue her career and take care of her home and children, all the while contributing to Britain’s high-tech economy. In addition to being a major project for the company, the Concorde was a symbol of British high-technology pride and prestige—it flew successfully for decades, the only supersonic passenger airplane to date. And Ann’s concurrent project functioned for even longer: the baby in the photograph is now fifty-three years old.


Prompt: Losing The Lead While Shirley, Moffatt, and hundreds of other women programmers created software that helped Britain advance further into the accelerating digital age, British industry and government struggled to hire, train, and retain their computer workers. Women had the technical skills, but were not supposed to be managers. Even the fact that women were wielding more power by controlling computers was viewed as dangerously out-of-bounds.

Response: As computing became increasingly interwoven with all of the functions of the state—from the Bank of England to the Atomic Energy Authority—computer workers grew indispensable. By the late 1960s, the government began to fear losing control of the machines that allowed the state to function because they did not have a well-trained, permanent, reliable core of technical experts. The women who had the technical skills were judged unreliable because they were not aligned with management. They were seen as liminally working-class, temporary workers who should not rise above their current station. To elevate women further would upend the hierarchies of both government and industry, pushing low-status workers into high-status positions.


Prompt: So determined were ministers within government that they needed a cadre of male, management-oriented technocrats that they began, counterintuitively and in desperation, to lower the standards of technical skill needed for the jobs. Lowering standards of technical proficiency to create an elite class of male computer workers didn’t work, however. In fact, it made the problem worse, by producing a devastating labor shortage.

Response: Well-heeled young men tapped for the positions often had no interest in derailing their management-bound careers by getting stuck in the “backwater” of computer work, which had still not fully shaken its association with low-level, feminized labor. Machine work in general was viewed as unintellectual and working-class, ensuring that men of the desired background had little interest in being swept up in the “industrialization of the office.” Most men who were trained for these positions, at great employer expense, left to take better, non-computing jobs within a year. As a result, the programming, systems analysis, and computer operating needs of government and industry went largely unmet. Although there were plenty of women who had the required skills, the government all but refused to hire them, and private industry largely refused to promote them.