Prompt: More public oversight is welcome, but insufficient. Regulating how data is extracted and refined is necessary. To democratize big data, however, we need to change who benefits from its use. Under the current model, data is owned largely by big companies and used for profit. Under a more democratic model, what would it look like instead? Again, the oil metaphor is useful. Developing countries have often embraced “resource nationalism”: the idea that a state should control the resources found within its borders, not foreign corporations. A famous example is Mexico: in 1938, President Lázaro Cárdenas nationalized the country’s oil reserves and expropriated the equipment of foreign-owned oil companies. “The oil is ours!” Mexicans cheered.

Response: Resource nationalism isn’t necessarily democratic. Revenues from nationalized resources can flow to dictators, cronies, and militaries. But they can also fund social welfare initiatives that empower working people to lead freer, more self-directed lives. The left-wing governments of Latin America’s “pink tide,” for instance, plowed resource revenues into education, healthcare, and a raft of anti-poverty programs.


Prompt: In a democracy, everyone should have the power to participate in the decisions that affect their lives. But that’s impossible if they don’t have access to the things they need to survive—and, further, to fulfill their full potential. Human potential is infinite. “You can be anything when you grow up,” parents tell their kids, a phrase we’ve heard so often it’s become a cliche—but which, when taken literally, is a genuinely radical thing to say. It’s a statement that would’ve been considered laughable for most of human history, and remains quite obviously untrue for the vast majority of the human race today.

Response: How could we make it true? In part, by giving people stuff. And this stuff can be financed out of the wealth that society holds and creates in common, including the natural wealth that Thomas Paine once called “the common property of the human race.” Nationalize It Data isn’t natural, but it’s no less a form of common property than oil or soil or copper. Resources that come from a planet we all happened to be born onto belong to everyone—they’re our “natural inheritance,” said Paine. Data is similar. Data is made collectively, and made valuable collectively.


Prompt: We all make data: as users, workers, consumers, borrowers, drivers. More broadly, we all make the reality that is recorded as data—we supply the something or someone to be recorded. Perhaps most importantly, we all make data meaningful together, because the whole point of big data is that interesting patterns emerge from collecting and analyzing large quantities of information.

Response: This is where the excessive emphasis on personal data is misleading. Personal data represents only one portion of the overall data pool. And even our personal data isn’t especially personal to the companies that acquire it: our information may have enormous significance for us, but it’s not particularly significant until it’s combined with lots of other people’s information.


Prompt: There’s a contradiction here, the most fundamental contradiction in capitalism: wealth is made collectively, but owned privately. We make data together, and make it meaningful together, but its value is captured by the companies that own it, and the investors who own those companies. We find ourselves in the position of a colonized country, our resources extracted to fill faraway pockets. Wealth that belongs to the many—wealth that could help feed, educate, house, and heal people—is used to enrich the few.

Response: The solution is to take up the template of resource nationalism, and nationalize our data reserves. This isn’t as abstract as it sounds. It would begin with the recognition, enshrined in law, that all of the data extracted within a country is the common property of everyone who lives in that country.


Prompt: Such a move wouldn’t necessarily require seizing the extractive apparatus itself. You don’t have to nationalize the data centers to nationalize the data. Companies could continue to extract and refine data—under democratically determined rules—but with the crucial distinction that they are doing so on our behalf, and for our benefit.

Response: In the oil industry, companies often sign “production sharing agreements” (PSAs) with governments. The government hires the company as a contractor to explore, develop, and produce the oil, but retains ownership of the oil itself. The company bears the cost and risk of the venture, and in exchange receives a portion of the revenue. The rest goes to the government.


Prompt: Production sharing agreements are particularly useful for governments that don’t have the machinery or expertise to exploit a resource themselves. This is certainly true in the case of big data: there is no government in the world that can match the capacity of the private sector. But governments have something the private sector doesn’t: the power to make and enforce laws. And they can use that power to ensure that data extractors pay for the privilege of making a profit from common property.

Response: The Data Dividend Bringing data revenues into public coffers is only the first step. To avoid the bad forms of resource nationalism, we would also need to distribute those revenues as widely as possible. In 1976, Alaska established a sovereign wealth fund with a share of the rents and royalties collected from oil companies drilling on state lands. Since 1982, the fund has paid out an annual dividend to every Alaskan citizen. The exact amount fluctuates with the fund’s performance, but in the last few years, it’s generally ranged from $1000 to $2000.


Prompt: We could do the same with data. In exchange for permission to extract and refine our data, companies would be required to pay a certain percentage of their data revenue into a sovereign wealth fund, either in cash or stock. The fund could use that capital to acquire other income-producing assets, as the Alaskan fund has, and pay out an annual dividend to all citizens. If it were generous enough, this dividend could even function as a universal basic income, along the lines of what Matt Bruenig has proposed.

Response: A data fund that distributes a data dividend would help democratize big data. It would enable us to collectively benefit from a resource we collectively create. It would transform data from a private asset stockpiled by corporations to make a small number of people rich into a form of social property held in common by everyone who helps create it.


Prompt: If we’re going to require companies to pay a chunk of their data revenue into a fund, however, we first have to measure that revenue. This isn’t always easy. A company like Facebook, by virtue of its business model, is wholly dependent on data extraction—all its revenue is data revenue. But most companies don’t fall into that category.

Response: Boeing, for instance, uses big data to help manufacture and maintain its planes. A 787 can produce more than half a terabyte of data per flight, thanks to sensors attached to various components like the engines and the landing gear. This information is then analyzed for insights into how to better preserve existing planes and build new ones. So, how much of Boeing’s total revenue is derived from data? Further, how much of a company’s data revenue can be attributed to one country? Big data is global, after all. If an interaction between an American and a Brazilian generates data for Facebook, where was that data extracted? And if Facebook then refines that data by combining it with information sourced from dozens of other countries, how much of the value that’s subsequently created should be considered taxable for our data fund? Measuring data’s value can be tricky. Fortunately, scholars are developing tools for it. And politics can help: in the past, political necessity has motivated the creation of new economic measurements. In the 1930s, the economist Simon Kuznets laid the basis for modern GDP because FDR needed to measure how badly the Great Depression had hurt the economy in order to justify the New Deal.


Prompt: Economic measurement doesn’t happen in a vacuum. Political power helps determine which parts of the economy are worth measuring, and how those measurements are understood. If we can build enough power to make our data ours, we can build enough power to measure what it’s worth. It’s Complicated Every analogy breaks down eventually. Thinking about data as the new oil takes us a fair distance towards understanding how it works, how to regulate it, and how to socialize it.

Response: But data is also very different than oil, or any other resource. That’s because it has genuinely radical potential. It’s not just a source of profit—it’s also, possibly, a mechanism for moving beyond profit as the organizing principle of our economic life. Maybe the most intriguing idea from the Marxist tradition is that capitalism creates the conditions for its overcoming—that the building blocks for making a better world are already present in our own. Information technology is almost certainly one of those building blocks. Data gives capitalism a new way to grow, yes, but it also might give us a way to turn capitalism into something else.


Prompt: One of capitalism’s sustaining myths is that it’s unplanned. Markets impartially, impersonally allocate wealth; Detroit goes bankrupt, Jeff Bezos makes another billion dollars, all because of something called the market. In truth, however, capitalism is planned. The planners are banks and other large financial institutions, as the economist J.W. Mason has pointed out—they make the decisions about how to allocate wealth, and their decisions are anything but impartial or impersonal.

Response: What if those decisions were democratic? What if everyone had the power to help make them? Such an economy would still be planned, of course. But planning would have to become more explicit and more participatory. This would also presumably change what an economy is for: if everyone had a say over how society organizes its wealth, the economy would no longer be run solely for the purpose of profit-making. It would become a machine for fulfilling human needs.


Prompt: Fulfilling human needs is a daunting task. After all, people’s needs vary. We all share some big ones, like the need for food, shelter, healthcare, and a habitable planet. But beyond the basics, needs can get pretty varied. For that reason, democratic planning is likely to be more complex than the capitalist variety. Drug addicts often talk about the clarity of addiction, how it simplifies one’s life by structuring it around a single goal: scoring the next dose. The clarity of capitalism is similar: it structures the economy around profit-making. In a society without this compulsion, the economy becomes less simple. Planning no longer serves a single goal, but many.

Response: This is where data comes in. Information technology has the potential to be planning’s killer app. It offers tools for meeting the complexity of the task, by enlarging our capacities for economic coordination. Better Laid Plans The idea of using computers to plan an economy isn’t new. The Soviets briefly experimented with it in the 1960s, Salvador Allende’s Chile explored it in the 1970s, and Western leftists have been particularly interested in it since the 1990s. In 1993, W. Paul Cockshott and Allin Cottrell published Towards a New Socialism, which proposed that advances in computing made a more efficient, flexible, and liberating form of planning possible—a theme picked up by more recent “accelerationist” works like Inventing the Future by Nick Srnicek and Alex Williams.


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

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


Prompt: Even better, much of this process could be automated. Economic democracy has the potential to be terribly time-consuming. Everyone should have the opportunity to participate in the decisions that most affect them, but nobody wants to make every decision. Nick Srnicek and Alex Williams offer one possible solution: rather than subject every last detail of the economy to democratic deliberation, we could come up with our preferred outcomes—“energy input, carbon output, level of inequality, level of research investment and so on”—and let the algorithms worry about how to get there.

Response: This is an excellent future, and an entirely feasible one. But it’s far from guaranteed. Transparency, coordination, automation—if these have democratic possibilities, they have authoritarian ones as well. China is likely to be the innovator in this respect. The government is developing a “Social Credit System” that uses big data to rate citizens’ “trustworthiness.” China also happens to be investing heavily in big data and artificial intelligence, which suggests that more sophisticated forms of surveillance and control will soon emerge.


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

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


Prompt: At present, venture capital deploys roughly $84 billion per year in the United States. And venture capital is no longer solely an American phenomenon. The model has been replicated all over the world, especially in burgeoning Asian tech ecosystems from Beijing to Bangalore. Yet relatively few people understand how venture works or where it comes from.

Response: Scaling to Survive Venture capital needs scale to survive. It needs to fund companies that have the potential to become very big in order to compensate for losses or break-even returns elsewhere in a portfolio. This need for scale is a key part of what distinguishes venture from other kinds of capital. Venture specializes in funding the development of unproven applications of technology—an endeavor that poses large risks with the potential for large rewards. Banks, commercial lenders, and traditional sources of capital for small businesses are highly restricted in the types of borrowers that they can afford to take risk on. Venture comes in to finance companies that traditional lenders can’t. And they do so through equity investments, rather than debt.


Prompt: In exchange for assuming greater risk, venture firms expect higher returns. Its model tolerates losses—sometimes obscene ones—for a chance at grabbing an entire market or customer “mindshare” first. Since some of the companies a VC fund invests in will fail, the ones that succeed must succeed big time. The venture model requires large, disproportionate returns from a handful of investments. About 6 percent of investments generate about 60 percent of venture capital’s total returns, according to a data set covering thirty years of returns from Horsley Bridge Partners, a firm that has invested in many well-known venture funds. Indeed, the larger the fund size or amount of capital under management, the larger the expectation of a big “exit”—an IPO or acquisition by another company.

Response: A fund that has $100 million may only be able to justify investing in companies that seem likely to result in an exit that pays out $1 billion or more. And since most VC funds are structured as ten-year partnerships, they’re not only looking for big exits—they’re looking for big exits on schedule. The VC model isn’t right for everyone. Many companies will never match the “return profile”—the rate of growth—required by venture capital. Moreover, for a startup, a venture check is only the beginning. Even though venture capital rounds are often celebrated as major milestones, they are really just entry tickets. When a company accepts venture funding, it commits itself to steep expectations for future growth. 2X a year is considered good, 3X is great, and 4X or more a year is amazing. These are challenging targets to meet, and the expectation to grow quickly can scale—but also contort—a founder’s original vision.


Prompt: Taking venture funding can also involve surrendering a certain amount of control, although the way that venture firms exert influence varies widely. At an early-stage firm like the one where I work, we engage in so-called “soft advising.” By contrast, venture firms that invest at a later stage usually hold seats on the company’s board, and can therefore exercise “hard control.” Sometimes this control is especially stark. In an earlier era, as many as 50 percent of original founders were thrown out in favor of professional management, according to an interview with Sequoia Capital founder Don Valentine. For the past decade, however, there has been a trend toward letting founders keep more power. The Facebook IPO set a new precedent by enabling Mark Zuckerberg to own most of the voting shares. Afterwards, venture firms marketed themselves as founder-friendly so as not to miss out on deals. But more recently, in the wake of the Uber crisis around Travis Kalanick, there is now some discussion that the industry has overcorrected toward too much founder control.

Response: The Masters of the Masters of the Universe To a startup founder seeking financing, venture capitalists might look like masters of the universe. But they answer to higher masters in the form of “limited partners.” These are the masters of the masters of the universe—venture capital’s customers, who supply most of the capital for a firm’s different funds.


Prompt: Venture capitalists don’t just provide capital, in other words. They also have to raise it. A firm’s managers—called “general partners”—are responsible for finding limited partners to finance investments. They also often have to invest a meaningful amount of their personal wealth in the fund they work for, so that limited partners are assured they have skin in the game.

Response: Historically, limited partners have included major pension funds, endowments, and other large institutions. The city of San Francisco, for example, has $1 billion out of its $20 billion under management allocated to venture capital to fund the retirements of city workers. Even if these investment officers are leading mission-driven or civic institutions like philanthropic foundations or state retiree funds, they are legally bound to do what is in their client’s best financial interest—regardless of whether the side effects of generating those returns may conflict with the ostensible values of the institution’s beneficiaries. As global wealth inequality has deepened, venture firms are also increasingly raising funds from “family offices,” which manage the private wealth of the world’s richest families.


Prompt: Every two to four years, venture capital firms embark on a fundraising roadshow. General partners pitch chief investment officers on their ability to secure exclusive access to a high-quality “deal flow” while being responsible stewards of capital for stakeholders like retirees or charitable foundations.

Response: The dynamic isn’t all that different from that of a hopeful entrepreneur seeking seed investment from a few dozen venture firms. Venture firms raise money from institutions, so that founders can raise money from them. The pressure to scale moves up the chain: a startup has to make it big in order to deliver large returns for its venture investor, so that its venture investor can deliver large returns to its limited partners.


Prompt: In order to succeed, venture firms pursue all kinds of strategies. They can make concentrated bets with high ownership stakes in a small number of companies. They can use their reputation to do highly selective momentum-based bets on growth-stage companies. They can “spray and pray”—go for smaller participation in a wider net of hundreds of companies. They can orient their entire fund around a specific theme like AI or robotics, or a geographic area like the Midwest.

Response: Depending on the strategy, the payoff may arrive sooner or later in the fund’s lifetime. Because returns aren’t realized for years, it can be hard to know whether a fund is high-performing at first. The privately held companies that a venture fund invests in don’t have the same reporting requirements as public companies, so it’s not simple to judge how a portfolio is doing overall.


Prompt: However it’s achieved, the highest return comes from perceiving value in ideas and people that the rest of the world doesn’t see yet. While venture investors say no a lot more than they say yes, one can never totally rule anything out. After all, businesses that were unworkable in the first dot-com boom (Webvan) became workable in the second one (Instacart) because of mobile phones and greater familiarity with e-commerce. There is always a chance that a bad idea in one business cycle becomes a brilliant idea in the next.

Response: These brilliant ideas aren’t evenly distributed across the industry, however. A small number of venture firms have disproportionately high returns compared to the rest. Across the entire industry, annual venture capital returns over the past ten years have been 9.1 percent compared to 7.9 percent for the S&P 500, which doesn’t look impressive. But the top quartile of funds might return something north of 20 percent as once-a-decade companies like Google or Facebook blow returns from other companies and funds out of the water.


Prompt: Traitors In order to understand venture capital, you have to understand not only how it works, but where it comes from. Venture capital emerged from a particular moment in American history, when military funding for technological development was waning, and investors were pioneering new ways of managing risk.

Response: The story of venture capital began at a semiconductor company called Shockley Semiconductor Laboratory in Mountain View, California. In the late 1950s, a group of eight physicists and electrical engineers who had been working at the company got fed up with their tyrannical boss, Nobel Prize winner William Shockley, and quit. But after they left Shockley, they faced a problem: there were few competing employers that could hire them. If they wanted to stay in the Bay Area instead of going back to the East Coast, they would have to start a company themselves.


Prompt: A young securities analyst named Arthur Rock tried to find funding for them. He pitched thirty large aerospace and electronics companies. All refused. The only place they could secure financing from was a failing aerial camera manufacturer named Fairchild. Its founder, Sherman Fairchild, was an eccentric bachelor who happened to be a prolific inventor, heir to the IBM fortune, and sympathetic to their cause.

Response: They eventually closed the deal. Those eight engineers—the “Traitorous Eight”—would go on to form Fairchild Semiconductor through a $1.38 million loan from Fairchild Camera for their first eighteen months of operation. And Rock would in turn become one of Silicon Valley’s very first venture capitalists. He would later move to the Bay Area in 1961 and realize $90 million of proceeds from an initial $3 million investment in companies like Teledyne and Scientific Data Systems. Meanwhile, the alumni of Fairchild Semiconductor—or  “Fairchildren,” as they were called—would go on to include Intel cofounders Robert Noyce and Gordon Moore, Sequoia Capital founder Don Valentine, and Kleiner Perkins cofounder Eugene Kleiner.


Prompt: The birth of venture capital came at an opportune time. Military funding for technology companies began receding in the late 1950s and early 1960s, creating a crisis for a young Silicon Valley still heavily dependent on military money. Just a few years after the historic Fairchild deal, Defense Secretary Robert McNamara began reducing weapons systems orders under the Kennedy Administration. This triggered a severe slowdown in the semiconductor industry known as the “McNamara Depression.” The loss of revenue from large-scale military contracts reordered the entire industry. It forced Silicon Valley companies like Fairchild to shift toward selling transistors and diodes in the civilian market. It also compelled them to cut labor costs by looking abroad—particularly to East Asia. In 1963, Fairchild opened an assembly plant in Hong Kong, where the hourly rate was 25 cents an hour, compared to $2.80 in the Bay Area.

Response: Fairchild eventually turned around, reaching $100 million in sales in 1964. But its success created a whole new set of problems, fueling friction between the company’s West Coast engineers and its East Coast management. These tensions eventually led to the exit of two of the original Traitorous Eight, Robert Noyce and Gordon Moore. Noyce and Moore turned to Arthur Rock to raise capital for their next company. That would be Intel—the $220 billion giant whose chips still run 80 percent of the world’s PCs.


Prompt: A few years later, two of the longest-standing venture firms came into existence. A young manager at HP Labs, Tom Perkins, teamed up with another of the Traitorous Eight, Eugene Kleiner, to create Kleiner Perkins—the storied firm that would back Amazon, Google, and Genentech, and then be pulled into a public trial during the Ellen Pao case four decades later. Yet another of the “Fairchildren,” Don Valentine, would go on to create Sequoia Capital, an eventual investor in Apple, Google, Oracle, and PayPal.

Response: Rethinking Risk By the early 1970s, venture capital had established itself in Silicon Valley. But it remained small: when Kleiner Perkins and Sequoia Capital closed their first funds, there was maybe $50 million devoted to venture in the whole of the United States. A limiting factor, enshrined in law, effectively capped the size of the venture industry. For venture capital to fill the void left by the decrease in defense contracts, and nourish a new tech industry, the law would have to be changed.


Prompt: In 1830, Harvard College sued a man named Francis Amory. Amory was accused of mismanaging funds that were intended to be donated to the university once a donor and his wife had passed away. Amory put the money into riskier investments like insurance, manufacturing, and banking stocks—and then lost half the money. Harvard sued and the judge sided with the college against Amory, establishing the “prudent man rule.” The prudent man rule required that investment managers exercise caution and care, avoid speculation, and behave as if the money they are investing were their own. In the 1960s and early 1970s, as the first venture capital firms were getting established, the prudent man rule was interpreted as a restriction on the kinds of risks that early-stage investors could take. This norm effectively prevented venture firms from raising capital from large institutional investors, which capped the possible size of the industry.

Response: If venture capital was going to grow, it would need a different interpretation of prudent investing. It would need to change how investors managed risk. In the late 1970s, venture capital scored a major victory. Congress was crafting a comprehensive set of rules governing private pension management to ensure that workers could feel secure that their retirement savings were being managed responsibly. They applied the prudent man rule to pension investments, which the investment management industry interpreted as a ban on riskier investments such as venture capital.


Prompt: But a Ford Foundation vice president named Roger Kennedy took it upon himself to convince policymakers that it was “prudent” to invest not only in low-risk bonds, but in higher-risk equities. He wanted to create a standard of prudence that took into account the whole composition of the portfolio rather than individual investments. A Nobel Prize-winning economist and student of Milton Friedman named Harry Markowitz had developed a similar idea as part of “modern portfolio theory.” This would be a fundamental shift from a principle of risk avoidance to one of risk management.

Response: So Kennedy and his allies lobbied to make the prudent man rule more flexible. The political climate helped: the stagflation crisis of the 1970s, along with rising anxiety about emerging competitors like Japan, stoked fears among policymakers that the country would fall behind if it didn’t stimulate new business development.


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

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


Prompt: Rocket Fuel, With Strings Attached What’s next for venture capital? In the past decade, Silicon Valley’s venture model has scaled globally. Venture has been exported all over the world, with China committing $50 billion to the U.S.’s $84 billion in 2017. Meanwhile, India, which had just over 100 million internet users five years ago, is now a bonafide market with roughly a half-billion people online and between $5 to $7 billion committed each year to venture-backed companies.

Response: Several factors are fueling venture’s global growth. As internet penetration has deepened, an increasingly unified market of more than 3 billion online consumers has made it easier for nascent businesses to quickly grow and acquire millions, if not hundreds of millions, of customers. And in the post-2008 world of low interest rates and cheaper access to capital, there is a lot of money out there looking for higher returns, leading many investors to venture on top of many other kinds of investment products. Moreover, new capital sources from Saudi Arabia’s sovereign wealth fund to Japanese corporation SoftBank’s Vision Fund have offered an influx of additional funding at the very late stage.


Prompt: But there are challenges ahead. This includes a record level of capital entering the industry and chasing a small number of venture-backable companies, which exerts downward pressure on returns as initial valuations increase. Moreover, plentiful capital lets companies stay private for longer, which pushes out exits for venture capital firms and their limited partners. Finally, the American venture industry remains highly geographically concentrated, with about half of all dollars invested going into the Bay Area.

Response: Venture also faces an interesting conundrum with the emergence of cryptocurrency-based initial coin offerings (ICOs). ICOs offer a relatively unregulated way for a project to crowdfund capital by selling “tokens,” which will appreciate if the value of the project is perceived to grow. The growth of ICOs has been explosive. In total, ICOs raised north of $4 billion in 2017, but their pace has been slowing down recently as lower-quality offerings and anticipation of SEC enforcement has made it harder for projects to reach their funding targets.


Prompt: That said, founders in the crypto space still aim to raise orders of magnitude more in capital for projects that haven’t even launched yet, compared to venture-backed founders that have spent years building a profitable business. Last year, a project named Block.one raised at least $700 million for its tokens—even though all it had produced was a fourteen-page white paper. The shopping startup Stitch Fix, by contrast, raised less than one-fifth as much during its recent IPO, even though the business pulled in nearly $1 billion in revenue this fiscal year.

Response: Whether ICOs will cannibalize part of the venture industry—or implode out of existence—is uncertain. What’s clear is that venture capital, or something conceptually like it, will continue to exist for the foreseeable future. Institutional investors that are managing billions of dollars of assets don’t have the time or bandwidth to evaluate tens of thousands of nascent projects to try to determine which ones might grow big enough to deliver large returns.


Prompt: That’s where venture comes in. It’s the intermediary that sifts through a glut of new and untested ideas to identify those that have the potential for massive scale—and then makes them fulfill that potential. For founders, it’s rocket fuel, with strings attached. It can help them reach entirely new markets, but it also sets up daunting expectations for scale that can alter their original vision—and have unintended consequences for society at large.

Response: Given the scale that Twitter is at, a one-in-a-million chance happens 500 times a day. It’s the same for other companies dealing at this sort of scale. For us, edge cases, those rare situations that are unlikely to occur, are more like norms. Say 99.999 percent of tweets pose no risk to anyone. There’s no threat involved… After you take out that 99.999 percent, that tiny percentage of tweets remaining works out to roughly 150,000 per month. The sheer scale of what we’re dealing with makes for a challenge.


Prompt: — Del Harvey, Vice President of Trust and Safety at Twitter, from “Protecting Twitter Users (Sometimes from Themselves),” March 2014. Scale and size, though, are not the same thing at all. If we think about simple massification, we might talk about the difference between a single cell and ten million cells, but that’s not the same as talking about a human arm. Scale is a dimension along which different objects arise; the arm exists as an object at one scale, the cell at another.

Response: —Paul Dourish, The Stuff of Bits: An Essay on the Materialities of Information (2017) Social media platforms must moderate, in some form or another. They must moderate to protect one user from another, or one group from its antagonists. They must moderate to remove offensive, vile, or illegal content. Finally, they must moderate to present their best face to new users, to their advertisers and partners, and to the public at large.


Prompt: Whether they want to or not, then, platforms must serve as setters of norms, interpreters of laws, arbiters of taste, adjudicators of disputes, and enforcers of whatever rules they choose to establish. Having in many ways taken custody of the web, they now find themselves its custodians. But the problem of moderation is not new. Broadcasters, booksellers, publishers, and music labels all grappled with the problem of being in the middle: not just between producer and audience, but between providing and restricting, between audience preference and public propriety. They have all had to set and enforce rules about what they will and will not make available.

Response: Moderation is not new to the web either. From the earliest days, users disagreed about what online spaces were for. Someone always wanted to circulate porn; someone always looked for ways to harass others. So parameters of proper content and behavior were always necessary. What is different about today’s social media platforms is that they host and oversee an unprecedented amount of content and an unprecedented number of people. As a result, they must moderate on a qualitatively different scale.


Prompt: From Workshop to Factory The most obvious difference is the sheer number of users, the sheer amount of content, and the relentless pace at which they circulate. It was once difficult to imagine how traditional media could review, rate, and police all of the US television programming running twenty-four hours a day across multiple channels. But that was a small challenge compared to what social media platforms must now do. As one policy manager at Flickr put it to me recently, “the scale is just unfathomable.” At this size, certain approaches to content moderation are practically impossible. For instance, there is simply too much content and activity to conduct “proactive review,” in which a moderator would examine each contribution before it appeared. Instead, nearly all platforms have embraced a “publish-then-filter” approach: user posts are immediately public, without review, and platforms can remove questionable content only after the fact.

Response: This means that everything, no matter how reprehensible or illegal, can be posted to these platforms and will be available until it is noticed and removed. Vile or criminal behavior may occur, and have its intended impact, before anything is done in response. There is always something on a social media platform that violates the rules—and typically lots of it. Someone is being harassed right now. Plenty of porn, graphic violence, animal cruelty, and hate speech are available as you read this. They will remain there for hours, days, or years, because of the challenges of policing platforms as immense as these. Because social media platforms operate at this scale, we as a society are being asked to tolerate the fact that even content as universally abhorred and clearly illegal as child pornography can be and is available on our favorite platforms, if only briefly.


Prompt: But the question of scale is more than just the sheer number of users. Social media platforms are not just big; at this scale, they become fundamentally different than they once were. They are qualitatively more complex. While these platforms may speak of their online “community,” singular, at a billion active users there can be no such thing. Platforms must manage multiple and shifting communities, across multiple nations and cultures and religions, each participating for different reasons, often with incommensurable values and aims. And communities do not independently coexist on a platform. Rather, they overlap and intermingle—by proximity, and by design.

Response: At this scale, moderation techniques that might have fit smaller venues simply will not translate. For instance, the techniques of online community management are ill-suited to the scale of major social media platforms. Managing early online communities depended in part on community members knowing the webmaster, regulars knowing one another, and users sharing an accumulated history of interactions that provided the familiarity and trust necessary for a moderator to arbitrate when members disagreed. Tough cases could be debated collectively; policies could be weighed and changed by the community. The scale of the forum made self- government possible. But as these platforms have grown, traditional community management has become increasingly untenable.


Prompt: This means that the approaches social media platforms take, toward not just content moderation but all types of information management, are tied to this immense scale. Content is policed at scale, and most complaints are fielded at scale. More important, the ways that moderators understand problems have been shaped by working at scale. As a content policy manager from Facebook noted: The huge scale of the platforms has robbed anyone who is at all acquainted with the torrent of reports coming in of the illusion that there was any such thing as a unique case… On any sufficiently large social network everything you could possibly imagine happens every week, right? So there are no hypothetical situations, and there are no cases that are different or really edgy. There’s no such thing as a true edge case. There’s just more and less frequent cases, all of which happen all the time.

Response: What to do with a questionable photo or a bad actor changes when you’re facing not one violation but hundreds exactly like it, and thousands much like it. This is not just a difference of size—it is fundamentally a different problem. For large-scale platforms, moderation is industrial, not artisanal.


Prompt: Industrial Moderation Industrial moderation requires industrial-scale human resources. These include community managers who work at the platform, crowdworkers who perform tasks farmed out to them, and users who avail themselves of complaint mechanisms. The labor that platforms put toward moderation, and the labor that we as users are conscripted to perform, are not just part of how platforms function—they constitute it. Platforms are made by the work that goes into content moderation. They are not platforms without it. But because this work is distributed among different labor forces, because it is unavailable to public or regulatory scrutiny, and because it is performed under high-pressure conditions, there is a great deal of room for slippage, distortion, and failure.

Response: In their earliest days, many platforms did not anticipate that content moderation would be a significant problem. Some began with relatively homogenous user populations who shared values and norms with one another and with the developers—like back when “TheFacebook” was only open to Ivy League university students. Many of the social norms that first emerged were familiar from college life, and the diversity of opinions, values, and intentions would be attenuated by the narrow band of people who were even there in the first place. Facebook, when it began, relied on Harvard students as volunteers, until the backlog of user complaints reached the tens of thousands. The company made its first permanent hire for content moderation in late 2005, almost eighteen months after launching.


Prompt: As these sites grew, so did the volume and variety of concerns coming from users. Platforms experienced these in waves, especially as they grew in cultural prominence, changed dramatically in their demographics, or expanded to an international audience. Some tried to address these concerns the same way that online discussion groups had: through collective deliberation and public rule-making. But this proved increasingly difficult.

Response: So the major social media platforms found that they had to expand their internal content policy groups. Today, most platforms have a team charged with overseeing moderation. The team sets the rules of the platform, supervises their enforcement, adjudicates the particularly hard cases, and crafts new policies in response.


Prompt: At the scale at which most platforms now operate, however, these internal teams would be insufficient by themselves. Back in 2009, 150 of Facebook’s then 850 employees—based in California and in Dublin, Ireland—handled moderation, one click at a time. But as Facebook expanded, such an artisanal approach became infeasible. So platforms turned to outsourcing much of the front-line work of content moderation to crowdworkers.

Response: As recently as 2014, Twitter was still claiming, “Every report by a user is reviewed by a member of Twitter’s Trust and Safety team.” Even for Twitter, which has leaner rules than similar-sized platforms, this statement is hard to believe—if what it meant was its handful of permanent employees devoted to content moderation.


Prompt: But Twitter, like many social media platforms, now employs a substantially larger group of people to provide a first wave of review, beneath the internal moderation team. These workers might be employed by the platform itself, either at its headquarters or in satellite offices located around the world in places like Dublin and Hyderabad.

Response: But more commonly they are hired on a contract basis: as independent contractors through third-party temp companies, or as on-demand labor employed through crowdwork services such as Amazon’s Mechanical Turk, Upwork, Accenture, or TaskUs—or both, in a two-tiered system. Crowdworkers are now used as a first-response team, looking at flagged posts and images from users and making quick decisions about how to respond.


Prompt: Human review must be handled fast. “Fast” can mean mere seconds per complaint—approve, reject, approve—and moderators are often evaluated on their speed as well as their accuracy, so they must keep up the pace. Each complaint is thus getting just a sliver of human attention, and the moderator is under enormous pressure to be responsive not just to the current complaint, but to the queue of complaints behind it. To speed things along, and to protect users’ privacy, the images and posts are also usually detached from their original material.

Response: Users expect the judgments of moderators to weigh competing values, show cultural sensitivity, and appear fair and consistent. This is hard to achieve when the content is distilled from its context, and the labor is performed under the intense pressure of an endless queue of complaints. But crowdworkers aren’t the only labor pool that platforms can draw on to perform moderation work. Platforms have another resource close at hand: the users themselves.


Prompt: Most platforms invite users to “flag” problematic content and behavior, generating a queue of complaints that can be fed to its army of crowdworkers to adjudicate. Flagging puts the work of review right at the point of offense, in front of those most motivated to complain. Even if moderators can’t be everywhere, users can. If internal content policy teams cannot anticipate emerging problems, users can serve as their nerve endings.

Response: Flaggers may also help an internal team better understand the norms of the community. Content that is not flagged will remain, even if members of the team might have worried about it had they encountered it. Similarly, they may look twice at materials they think are acceptable but that users keep flagging. This becomes especially useful as a site expands into different countries: the population of possible flaggers automatically includes people with the necessary linguistic and cultural expertise.


Prompt: Logistics and Translations Each of these three labor forces—the internal team, the crowdworkers, and the flaggers—is an answer to the problem of moderation at scale. Each provides a way for either a few people to do a great deal with limited resources, or for a lot of people to each do a little, together.

Response: The challenge of content moderation, then, is as much about the coordination of work as it is about making judgments. What the press or disgruntled users might see as mistaken or hypocritical might be the result of slippage between these divisions of labor—between what is allowed and what is flagged; between how a policy is set and how it is conveyed to a fluctuating population of crowdworkers; between how a violation is understood in one cultural climate and how it is understood in another; between what does trigger a complaint and what should.


Prompt: The challenges of coordinating the distributed and decentralized work of moderation are probably most acute at the points of contact. Between flaggers and crowdworkers, and between crowdworkers and policy teams, are membranes across which must flow expressions of principle in one direction and expressions of concern in the other. At these points of contact, expressions get rewritten, translated into a new form that is meant to both retain the meaning and fit it to the work that will respond to it. These translations can introduce distortions, ambiguities, and even new meanings.

Response: For example, enlisting the crowd to police itself can only work if the concerns of users can be transformed into manageable bits of data. When a user flags some problematic content, the character of his concern must be reduced to fit the data entry format provided: the flag itself, its particular submenus, time codes, and so on. This takes an affective, socially loaded, and meaningful expression of a user, scrubs it of emotion and detail, and restates it.


Prompt: What may have been “Ugh! That’s terrible!” becomes “flag :: hateful or abusive content :: promotes hatred and violence :: 5:40.” The data comes to stand in for the users and their objections. Something is lost, and something is added. Similarly, when policy teams craft specific guidance for crowdworkers, they must translate the platform’s community guidelines into instructions that will suit the modular, rapid work of the reviewers. But these two documents are not congruent in their spirit, emphasis, or detail. Again, much is lost and much is added.

Response: The Robots Aren’t Coming In May 2016, TechCrunch reported a significant milestone in how Facebook handles moderation: “Facebook’s artificial intelligence systems now report more offensive photos than humans do.” This does not mean that Facebook is using automatic techniques to remove photos, as some of the press coverage that followed incorrectly assumed. The platform’s automatic detection software is designed to detect nudity, hate speech, and the like—to identify it and flag it for human review. Still, the fact that the majority of what is reviewed is flagged by software speaks to how important automated techniques for moderation are becoming.


Prompt: Is AI the answer? Software-based detection techniques promise, first, to solve the problem of scale. Particularly for sites that are too vast, like YouTube, or that emphasize real-time communication, like Twitter, platforms would like to have moderation techniques that do not depend on direct and real-time human oversight, and that can immediately and automatically identify unacceptable content. Ideally, these automated methods could be paired with automated interventions: algorithmically identified porn or hate speech would be instantly removed or withheld from some users.

Response: Automated detection isn’t easy, however. Arguably, it’s impossible, given that what constitutes an offense depends so critically on both interpretation and context. State-of-the-art detection algorithms have a difficult time discerning offensive content or behavior even when they know precisely what they are looking for. There are fundamental limitations that may be impossible to overcome: the lack of context, the evasive tactics of users, and the fluid nature of offense.


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

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


Prompt: The stakes for false positives and false negatives differ, depending on the context and audience. While marking the occasional baby photo as pornography is one kind of problem for the users involved, incorrectly identifying black skin in ways systemically different from white skin is a different kind of problem—a public problem about representation and equity, rather than a consumer problem about efficiency and inconvenience.

Response: These platforms now function at a scale and under a set of expectations that increasingly demand automation. Yet the kinds of decisions that platforms must make, especially in content moderation, are precisely the kinds of decisions that should not be automated, and perhaps cannot be. They are judgments of value, meaning, importance, and offense. They depend both on a human revulsion to the horrific and a human sensitivity to contested cultural values.


Prompt: There is, in many cases, no right answer for what to allow or disallow, except in relation to specific individuals, communities, or nations that have debated and regulated standards of propriety and legality. And even then, the edges of what is considered appropriate are constantly recontested, and the values they represent are always shifting.

Response: Feeling Like a Data Point No matter how they handle content moderation, what their politics and premises are, or what tactics they choose, platforms must work at an impersonal scale: the scale of data. Platforms must treat users as data points, subpopulations, and statistics, and their interventions must be semi-automated so as to keep up with the relentless pace of both violations and complaints. This is not customer service or community management but logistics—where concerns must be addressed not individually, but procedurally.


Prompt: However, the user experiences moderation very differently. Even if a user knows, intellectually, that moderation is an industrial-sized effort, it feels like it happens on an intimate scale. “This is happening to me; I am under attack; I feel unsafe. Why won’t someone do something about this?” Or, “That’s my post you deleted; my account you suspended. What did I do that was so wrong?” The press also tends to cover moderation at this intimate scale. Journalists challenge the actions of a behemoth company by pointing out a single violation, and telling the story of the people who were harmed by it. Compelling, human-sized stories fit the journalistic frame well, but they say little about the scale at which social media platforms operate.

Response: It may be that this gulf can be bridged. Users may become more aware of this sociotechnical apparatus and their place in it; social media platforms might better recognize that impersonal interventions make users feel personally judged or silenced. On the other hand, it may be that these scales are simply irreconcilable. Interventions made at a data scale may always run up against the lived experience of those interventions, felt intimately by individuals.


Prompt: Trawling a hotel minibar one night while on a work trip to Amsterdam, I found a piece of chocolate with an unusual name: Tony’s Chocolonely. I giggled at how apt the name was—who eats minibar chocolate unless they are, indeed, a little lonely?—and, on a whim, plugged it into Google. The results were more sobering than I’d expected. The founder of Chocolonely, Teun (Tony) van de Keuken, founded the company with the goal of making the first (the “lonely only”) chocolate bar produced without labor exploitation. According to the company, this goal actually landed them in legal trouble: Bellissimo, a Swiss chocolatier, sued Chocolonely in 2007, allegedly claiming that “slave-free chocolate is impossible to produce.” I had heard similar claims about other industries. There was the Fairphone, which aimed at its launch in 2013 to be the first ethically produced smartphone, but admitted that no one could guarantee a supply chain completely free from unfair labor practices. And of course one often hears about exploitative labor practices cropping up in the supply chains of companies like Apple and Samsung: companies that say they make every effort to monitor labor conditions in their factories.

Response: Putting aside my cynicism for the moment, I wondered: What if we take these companies at their word? What if it is truly impossible to get a handle on the entirety of a supply chain? The thing that still confused me is how reliable supply chains are, or seem to be. The world is unpredictable—you’ve got earthquakes, labor strikes, mudslides, every conceivable tragedy—and yet as a consumer I can pretty much count on getting what I want whenever I want it. How can it be possible to predict a package’s arrival down to the hour, yet know almost nothing about the conditions of its manufacture? In the past twenty years, popular and academic audiences have taken a growing interest in the physical infrastructure of global supply chains. The journalist Alexis Madrigal’s Containers podcast took on the question of how goods travel so far, so quickly. The writer Rose George traveled the world on a container ship for her book Ninety Percent of Everything. And Marc Levinson’s The Box startled Princeton University Press by becoming a national bestseller. Most recently, Deborah Cowen’s The Deadly Life of Logistics offered a surprisingly engrossing history of that all-important industry.


Prompt: These books help us visualize the physical infrastructure that makes global capitalism possible. But the data infrastructure has yet to be explored. How does information travel through the supply chain in such a peculiar way, so that I know to wait impatiently at my door at the exact moment my new iPhone will arrive—but no one really seems to know how it has gotten to me? I set out to find the answer, and what I found surprised me. We consumers are not the only ones afflicted with this selective blindness. The corporations that make use of supply chains experience it too. And this partial sight, erected on a massive scale, is what makes global capitalism possible.

Response: A Network of Waterways The industry of supply-chain management (or SCM, to its initiates) is both vast and secretive. It’s one of the most rapidly growing corporate fields, and the subject of reams of books, journal articles, and blog posts. You can even get a degree in it. But most companies are leery about revealing too much about their own logistics operations. It’s not only because they are afraid of exposing what dark secrets might lurk there. It’s also because a reliable, efficient supply chain can give a company an invaluable edge over its competitors.


Prompt: Take Amazon: it’s not so much a retailer as a supply chain incarnate. Its advantage lies in the high speed and the low price with which it can get a set of bath towels to your door. No wonder the retailer is famously tight-lipped about its supply-chain infrastructure. Few people outside of Amazon know much about the software that Amazon uses to manage its logistics operations.

Response: In the supply-chain universe, there are large, tech-forward companies like Amazon and Apple, which write and maintain their own supply-chain software, and there’s everyone else. And most everyone else uses SAP. SAP—the name stands for Systems, Applications, and Products—is a behemoth, less a single piece of software than a large, interlocking suite of applications, joined together through a shared database. Companies purchase SAP in “modules,” and the supply-chain module interlocks with the rest of the suite. Among people who’ve used SAP, the reaction to hearing its name is often a pronounced sigh—like all large-scale enterprise software, SAP has a reputation for being frustrating.


Prompt: Nevertheless, SAP is ubiquitous, with modules for finance, procurement, HR, and supply-chain management. “A very high percentage of companies run SAP for things like finance,” says Ethan Jewett, an SAP consultant and software developer who helps companies implement SAP modules. “And so, if you’re running it for one part of your business, you’ll default to running it for another part of your business.” Leonardo Bonanni is the founder and CEO of a company called Sourcemap, which aims to help companies map their own supply chains. Bonanni suspects that companies’ inability to visualize their own supply chain is partly a function of SAP’s architecture itself. “It’s funny, because the DNA of software really speaks through,” said Bonanni. “If you look at SAP, the database is still actually written in German. The relations in it are all one-link. They never intended for supply chains to involve so many people, and to be interesting to so many parts of the company.” This software, however imperfect, is crucial because supply chains are phenomenally complex, even for low-tech goods. A company may have a handle on the factories that manufacture finished products, but what about their suppliers? What about the suppliers’ suppliers? And what about the raw materials? “It’s a staggering kind of undertaking,” said Bonnani. “If you’re a small apparel company, then you still might have 50,000 suppliers in your supply chain. You’ll have a personal relationship with about 200 to 500 agents or intermediaries. If you had to be in touch with everybody who made everything, you would either have a very small selection of products you could sell or an incredible margin that would give you the extra staff to do that.” We call them “supply chains,” but that image is misleading. They really look more like a network of waterways, with thousands of tiny tributaries made up of sub-suppliers trickling into larger rivers of assembly, production, and distribution.

Response: Bonanni explained that while workplace abuses get a lot of attention when they take place in the supply chains of large, prestigious companies like Apple and Samsung, working conditions are actually most opaque and labor abuse is most rampant in other industries, like apparel and agriculture. “Apparel, every quarter they have 100 percent turnover in the clothing that they make, so it’s a whole new supply chain every season. And with food, there’s millions of farmers involved. So in these places, where there’s way too many nodes for anyone to see without a computer, and where the chain changes by the time you’ve monitored it—those are the places where we see a lot of problems and instability.” The picture that many of us have of supply chains involve state-of-the-art factories like those owned by Foxconn. In reality, the nodes of most modern supply chains look much less impressive: small, workshop-like outfits run out of garages and outbuildings. The proliferation and decentralization of these improvisational workshops help explain both why it’s hard for companies to understand their own supply chains, and why the supply chains themselves are so resilient. If a fire or a labor strike disables one node in a supply network, another outfit can just as easily slot in, without the company that commissioned the goods ever becoming aware of it.


Prompt: It’s not like there’s a control tower overseeing supply networks. Instead, each node has to talk only to its neighboring node, passing goods through a system that, considered in its entirety, is staggeringly complex. Supply chains are robust precisely because they’re decentralized and self-healing. In this way, these physical infrastructures distributed all over the world are very much like the invisible network that makes them possible: the internet.

Response: Gold Must Be Gold By the time goods surface as commodities to be handed through the chain, purchasing at scale demands that information about their origin and manufacture be stripped away. Ethan Jewett explained the problem to me in terms of a theoretical purchase of gold: In some sense all gold is the same, so you just buy the cheapest gold you can get. But if you look at it in another way, it matters how it was mined and transported. And then all of the sudden, every piece of gold is a little bit different. And so it becomes very difficult to compare these things that, in terms of your actual manufacturing process, are almost exactly the same.


Prompt: To be traded as a commodity, in other words, gold must be gold. As Jewett described this state of affairs, I felt a jolt of recognition. The system he was outlining was, in a word, modular: a method of partitioning information that’s familiar to every computer programmer and systems architect. Modular systems manage complexity by “black-boxing” information; that is, they separate code or information into discrete units. A programmer need only know about the module with which she is working, because managing the complexity of the entire system would be too much to ask of any single individual. Modularity is the method we’ve devised to manage complexity at a time when we’re drowning in information.

Response: The computing historian Andrew Russell told me that “black-boxing reduces all kinds of cognitive and informational overhead, because you just know what the box spits out; you don’t need to know anything about what’s going on in there.” Modularity, as Russell has documented, emerged as a term in architecture, and then spread to the military, where it was picked up to describe Project Tinkertoy, a post-World War II program to design interchangeable, self-contained parts for electronics. From there, the notion of modularity proliferated wildly, as a way of thinking about and structuring everything from organizations to economics to knitting. “It’s kind of a characteristic of modernity,” Russell said.


Prompt: Supply chains are highly modular by design. Think of the shipping container. It wasn’t revolutionary because it was a box; it was revolutionary because it was a standardized, interchangeable box that could be locked in and transported. It makes globalization possible—it makes global scale possible—because of what it obscures. One doesn’t need to know what’s in the box, just where it needs to go.

Response: How do you manage the complexity of a system that procures goods from a huge variety of locations? You make it modular: when you black-box each component, you don’t need to know anything about it except that it meets your specifications. Information about provenance, labor conditions, and environmental impact is unwieldy when the goal of your system is simply to procure and assemble goods quickly. “You could imagine a different way of doing things, so that you do know all of that,” said Russell, “so that your gaze is more immersive and continuous. But what that does is inhibit scale.” And scale, of course, is key to a globalized economy.


Prompt: On the one hand, this all seems very logical and straightforward: to manage complexity, we’ve learned to break objects and processes into interchangeable parts. But the consequences of this decision are wide-ranging and profound. It helps explain, for one thing, why it’s so hard to “see” down the branches of a supply network. It also helps explain why transnational labor organizing has been so difficult: to fit market demands, workshops have learned to make themselves interchangeable. It sometimes seems as though there’s a psychological way in which we’ve absorbed the lessons of modularity—although the world is more connected than ever, we seem to have trouble imagining and articulating how we’re linked to the other denizens of global manufacturing networks.

Response: Put It On the Blockchain If technology enables a selective blindness that makes the scale of global supply chains possible, can technology also cure the problem of disavowal? Can software, having created the black box, help crack it open? Recently, there’s been a lot of buzz about blockchain and the Internet of Things (IoT) among SCM practitioners. IoT technology would attach transmitters to components, so that their locations could be traced and monitored. With blockchain technology, each component that passes through a supply chain could have a unique, traceable ID number, and a log that registers every time it changes hands. Their proponents say that these technologies could bring radical transparency and unprecedented safety to global supply chains.


Prompt: Blockchain is the technology that underlies bitcoins. The idea is that at each “stop” along a chain of users, a database associated with a particular coin (or component) updates to register the change of hands. The identity of each user could be cryptographically concealed, or it could be recorded transparently. Either way, the record of transactions is available to everyone along the chain, and it’s near-impossible to forge.

Response: Blockchain is security for the age of decentralization, and it could, in theory, make it possible for companies to verify the safety, composition, and provenance of manufactured goods. Supply Chain 24/7, an industry newsletter, calls blockchain a “game-changer” that “has the potential to transform the supply chain.” IoT is a different technology that addresses a similar problem. A company somewhere along a supply chain embeds a small transmitter, like an active RFID tag, in a component, allowing a monitor to see its location and status in real time. With sensors, a company could also keep track of the component’s environment, checking on things like temperature and humidity. It sounds like a solution custom-fitted for the problem at hand: with these tiny trackers, companies could finally get the visibility they say they’re after.


Prompt: But the supply-chain specialists I talked to were skeptical. To make blockchain meaningful, Bonnani told me, you’d need to get every vendor to agree to disclose information about its practices; otherwise you’ll just see a string in a database. “If you get suppliers to agree to be transparent, then blockchain is a way to verify that the thing you receive actually came from the person who sent it to you, and it’s extremely valuable in that respect,” said Bonnani. “But if you don’t get them to opt in, then all you know is, you got what you asked for. They’re not going to tell you who they got it from, or who that person got it from.” IoT lends itself to the same problems. Without genuine buy-in from suppliers, IoT “becomes one more technology to counterfeit,” said Bonnani. “You’re basically not improving the current problem, which is a lack of visibility.” Given the pressure on suppliers to move quickly and flexibly, it’s hard to imagine anyone volunteering more information than necessary.

Response: One could imagine a system in which IoT and blockchain enable detailed information on labor conditions and safety, but the reality of global capitalism suggests that IoT is more likely to bring us smart toasters than socially responsible supply chains. Making Better Bets SCM innovation continues to thrive, but it’s not trending toward the kind of visibility that Tony’s Chocolonely is looking for. The newest technology that logistics professionals find exciting is machine learning, which involves creating algorithms that are capable of making predictions or decisions by “learning” from a set of data.


Prompt: Machine learning is already in heavy use on the consumer side, where companies like Target use it to wager that a shopper who purchases unscented lotion, vitamins, hand sanitizer, and soft furniture might be getting ready to have a baby. But in the SCM world, machine learning could make it much easier to discover which suppliers and routes will deliver goods most quickly and reliably. A company could “predict the performance of each supplier, carrier, forwarder, port, lane, road, manufacturing facility, warehouse, etc. within the extended supply chain, under varying conditions,” according to the SCM analytics company Transvoyant.

Response: In a machine-learning scenario, companies could use historical data about manufacturers and goods to assign suppliers risk scores. Weather, changes in a supplier’s political climate, or economic factors could all reassign risk scores, causing the supply network to automatically reconfigure itself to favor less risky suppliers. It’s a stunning idea: supply-chain networks dynamically rerouting themselves in response to global risk factors, just the way Google Maps sends you down surface streets when the freeway is clogged.


Prompt: This would increase efficiency, but at the cost of making it even more impossible to identify the supplier of your smartphone’s LCD screen. It would aggravate, not alleviate, the problem of selective blindness that’s already so deeply embedded in global supply chains. In reality, the prospect of using machine learning on the manufacturing end of supply chains remains mostly speculative. When a company doesn’t even know the most basic facts about its suppliers, it’s hard to imagine how it would assemble the data necessary to develop efficient machine-learning models.

Response: But its attraction for SCM specialists is notable, because it points to the kind of visibility that companies are talking about when they call for supply-chain transparency: not the kind of information that would help a consumer see where her candy comes from, but the kind of information that would get it into her hands faster and cheaper.


Prompt: Visibility, in the SCM context, is itself highly selective. Learning To See The challenges are political as well as technical, in other words. And the political challenges are immense. In the absence of real efforts to create democratic oversight of supply chains, we’ve come to see them as operating autonomously—more like natural forces than forces that we’ve created ourselves.

Response: In 2014, the Guardian reported that Burmese migrants were being forced into slavery to work aboard shrimp boats off the coast of Thailand. According to Logan Kock of Santa Monica Seafood, a large seafood importer, “the supply chain is quite cloudy, especially when it comes from offshore.” I was struck by Kock’s characterization of slavery as somehow climatological: something that can happen to supply chains, not just something that they themselves cause.


Prompt: But Kock was right, supply chains are murky—just in very specific ways. We’ve chosen scale, and the conceptual apparatus to manage it, at the expense of finer-grained knowledge that could make a more just and equitable arrangement possible. When a company like Santa Monica Seafood pleads ignorance of the labor and environmental abuses that plague its supply chains, I find myself inclined to believe it. It’s entirely possible to have an astoundingly effective supply chain while also knowing very little about it. Not only is it possible: it may be the enabling condition of capitalism at a global scale.

Response: It’s not as though these decentralized networks are inalterable facts of life. They look the way they do because we built them that way. It reminded me of something the anthropologist Anna Tsing has observed about Walmart. Tsing points out that Walmart demands perfect control over certain aspects of its supply chain, like price and delivery times, while at the same time refusing knowledge about other aspects, like labor practices and networks of subcontractors. Tsing wasn’t writing about data, but her point seems to apply just as well to the architecture of SAP’s supply-chain module: shaped as it is by business priorities, the software simply cannot absorb information about labor practices too far down the chain.


Prompt: This peculiar state of knowing-while-not-knowing is not the explicit choice of any individual company but a system that’s grown up to accommodate the variety of goods that we demand, and the speed with which we want them. It’s embedded in software, as well as in the container ships that are globalization’s most visible emblem.

Response: We know so much about the kinds of things we can get and when we can get them. But aside from the vague notion that our stuff comes from “overseas,” few of us can really pin down the stations of its manufacture. Is a more transparent—and more just—supply chain possible? Maybe. But, as the Chocolonely lawsuit demonstrates, it could mean assimilating a lot of information that companies have become very good at disavowing—a term that, in its Freudian sense, means refusing to see something that might traumatize us.


Prompt: In the future that was imagined during the Obama years, the technology industry was one of the most valiant heroes. As the confetti settled in 2008, Obama was celebrated as the “first internet president.” He was as natural in the medium as Kennedy had been in television. Four years later it was the campaign’s unprecedented data analytics strategy—one which mined personal information on tens of millions of Americans to target its messaging—that was framed as the secret of the president’s re-election.

Response: Obama was hailed as a modern, progressive figure. He was bringing Silicon Valley innovation into the heart of government. With that smile and those smarts, what could go wrong? Of course, in 2016, everything did go wrong. Hillary Clinton’s precision-guided electioneering efforts came up short. Donald Trump was elected president—thanks to many of the same targeting techniques pioneered by Obama, and a Russian-fed misinformation campaign across Facebook and Twitter. Ever since, Big Tech’s political fortunes have dimmed. It is now at risk of joining the ranks of Big Tobacco, Big Pharma, and Big Oil as a bona fide corporate pariah.


Prompt: Today, a growing number of people are thinking about how to regulate Big Tech. Think tanks are studying the complex interactions between social and technical systems, and proposing ways to make them fairer and more accountable. Journalists are investigating how platforms and algorithms actually function, and what’s at stake for people’s lives.

Response: But while all of this has been crucial in refocusing the public conversation, it needs to go further. We won’t fix Big Tech with better public policy alone. We also need better language. We need new metaphors, a new discourse, a new set of symbols that illuminate how these companies work, how they are rewiring our world, and how we as a democracy can respond.


Prompt: Imaginary Friends Tech companies themselves are very aware of the importance of language. For years, they have spent hundreds of millions of dollars in Washington and on public relations to preserve control of the narrative around their products. This clearly paid off: media coverage of the industry was largely favorable, until the 2016 election forced journalists to ask much more critical questions. The shift in sentiment has put the industry on the offensive and propelled lobbying spending to new heights, with Google paying out more than any other company in 2017.

Response: In its public messaging, Facebook describes itself as a “global community,” dedicated to deepening relationships and bringing loved ones closer together. Twitter is “the free speech wing of the free speech party,” where voices from all the world can engage with each other in a free flow of ideas. Google “organizes the world’s information,” serving it up with the benevolence of a librarian.


Prompt: However, in light of the last election and combined market valuations that stretch well into the trillions of dollars, this techno-utopian rhetoric strikes a disingenuous chord. What would a better language look like? Most of the regulatory conversations about platforms try to draw analogies to other forms of media. They compare political advertising on Facebook to political advertising on television, or argue that platforms should be treated like publishers, and bear liability for what they publish. However, there’s something qualitatively different about the scale and the reach of platforms that opens up a genuinely new set of concerns.

Response: At last count, Facebook had over 200 million users in the United States, meaning that its “community” overlaps to a profound degree with the national community at large. It is on platforms like Facebook where much of our public and private life now takes place. And, as the election makes clear, there is a substantial public interest in how these technologies are used. Their dominance means that a few big companies can determine what free speech and free association look like in these new, privately owned public spheres.


Prompt: The ground upon which politics happens has changed—yet our political language has not kept up. We know that we look at our phones too much and that we’re probably addicted to them. We know that basically every aspect of our life—from our passing curiosities to our most ingrained habits—are recorded in private databases and made valuable through the obscure alchemy of data science.

Response: But we don’t know how to talk about all of this as a whole or what it really adds up to. We lack a common public vocabulary to express our anxieties and to clearly name what has changed about how we communicate, how we relate to other people, and how we come to have any idea what’s going on in the world in the first place.


Prompt: The work being done by experts, insiders, and policymakers—whether in the form of the EU’s new regulatory regime to govern personal data, the General Data Protection Regulation, or various conversations within the tech community—is necessary but not sufficient. For a durable and democratic response to the power of platforms, we need a shared set of concepts that are rich enough to describe the new realities and imaginative enough to point towards a meaningfully better future.

Response: The Octopus The biggest insight in George Orwell’s 1984 was not about the role of surveillance in totalitarian regimes, but rather the primacy of language in shaping our sense of reality and our ability to act within it. In the book’s dystopian world, the Party continuously revises the dictionary, removing words as they try to extinguish the expressive potential of language. Their goal is to make it impossible for vague senses of dread and dissatisfaction to find linguistic form and evolve into politically actionable concepts.


Prompt: If you can’t name and describe an injustice, then you will have an extremely difficult time fighting it. Making the world thinkable to a democratic public—and thus empowering them to transform it—is a revolutionary act. In the late nineteenth century, the United States was in a situation similar to today. The rapid rise of industrialization changed the social fabric of the country and concentrated immense power over nearly every facet of the economy in the hands of a few individuals. The first laws to regulate industrial monopolies came on the books in the 1860s to protect farmers from railroad price-gouging, but it wasn’t until 1911 that the federal government used the Sherman Antitrust Act to break up one of the country’s biggest monopolies: Standard Oil.

Response: In the intervening fifty years, a tremendous amount of political work had to happen. Among other things, this involved broad-based consciousness building: it was essential to get the public to understand how these historically unprecedented industrial monopolies were bad for ordinary people and how to reassert control over them.


Prompt: Political cartoons offered an indispensable tool in this imaginative struggle by providing a rich set of symbols for thinking about the problems with unaccountable and overly centralized corporate power. Frequently pictured in these cartoons were the era’s big industrialists—John Rockefeller, Cornelius Vanderbilt, Andrew Carnegie—generally pictured as plump in their frock coats, resting comfortably on the backs of the working class while Washington politicians snuggled in their pockets. And the most popular symbol of monopoly was the octopus, its tentacles pulling small-town industry, savings banks, railroads, and Congress itself into its clutches.

Response: The octopus was a brilliant metaphor. It provided a capacious but simple way to understand the deep interconnection between complex political and economic forces— while viscerally expressing why everyone was feeling so squeezed. It worked at both an analytical and an emotional level: it helped people visualize the hidden relationships that sustained monopoly power, and it cast that power in the form of a fearsome monster. Conveyed as a lively cartoon, its critique immediately connected.


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

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


Prompt: A hint of where we may find this new political language recently appeared in the form of the White Collar Crime Risk Zones app from The New Inquiry. The app applies the same techniques used in predictive-policing algorithms—profoundly flawed technologies which tend to criminalize being a poor person of color—to white-collar crime.

Response: Seeing the business districts of Manhattan and San Francisco blaze red, with mugshots of suspects generated from LinkedIn profile photos of largely white professionals, makes its point in short order. It seems absurd—until you realize that it is exactly what happens in poor communities of color, with crushing consequences. What if police started frisking white guys in suits downtown? Much like the political cartoons of the nineteenth century, the White Collar Crime Risk Zones app is effective because it uses the vernacular language of software. It functions as a piece of rhetorical software: it’s not designed to collect data or to sell advertising, but to make an argument. And while software will never be the solution to our political and social problems, it may, to hijack a slogan of Big Tech, at least provide a way to “think different.” A single E. coli placed in a sugary broth will divide into two genetically identical cells in twenty minutes. Another twenty minutes later, those two will grow and divide into four. Give those cells enough broth and room to grow and after about twenty-three hours there will be enough E. coli cells to fill an Olympic-size swimming pool. Wait another twenty hours and the mass of bacteria would equal the volume of the earth. Round it out to forty-eight hours and you’d have a ball of E. coli twenty-two times the size of Jupiter.


Prompt: The scales possible with exponential growth are as incomprehensible as they are impossible. E. coli’s potential for exponential doubling is realized only in the highly controlled environment of a researcher’s test tube, where food is abundant and no other species are in the way. Even then, the time that bacteria can be expected to grow exponentially is inevitably limited—growth slows once nutrients start running low or the cells are too crowded to keep dividing further.

Response: Biological growth scales to fit its context. There are no gas giants full of identical E. coli. More than one hundred years ago, D’Arcy Wentworth Thompson published On Growth and Form, a treatise on the mathematics of biological growth. In it, he summarizes this central maxim for biological scaling: “The effect of scale depends not on a thing in itself but in relation to its whole environment or milieu; it is in conformity with the thing’s ‘place in Nature,’ its field of action and reaction in the Universe.” Biology’s ability to grow in relation to its environment—to grow ecologically rather than exponentially—is at the heart of what inspires biologists, engineers, and designers to work with organisms to build a new kind of technology. What if our technologies could grow and heal like living things? What if concrete could be set by the metabolism of microbes, any cracks repaired in situ? What if factories were replaced with farms, growing new things that could be recycled back into the soil? What if, as MIT Media Lab director Joi Ito has proposed and further developed in a recent manifesto, “the role of science and technology [in the next hundred years] will be about becoming part of nature rather than trying to control it”? People don’t multiply like E. coli, but put a few people together and you’ll quickly end up with more. Over the scale of millennia we’ve ended up with 7,589,052,176—the mind-boggling number of people on earth today, whose ability to satisfy basic needs for shelter, food, and warmth are always dependent on the availability and distribution of resources.


Prompt: How we’ve grown to this number is a story about the industrial revolution, where the convergence of steam power, chemical development, mechanized production and the ideas revolution of the Age of Enlightenment led to great leaps and bounds in our capacity to create life-giving stuff—and therefore the conditions for populations to multiply. We are currently riding that exponential growth curve, and the UN expects this to peak at 11 billion by the end of the century. Within our current economic framework, that’s 11 billion consumers of life-giving resources, taking from living ecosystems without regard to the environmental milieu. Industrialization has put manufacturing on exponential curves that can be as incomprehensible as the growth of E. coli—and as devastating on a global scale.

Response: The Re-industrial Revolution Each of the 7,589,052,176 people currently on the planet uses an average of 1700 litres of water every day. This is partly for drinking and washing, but it’s mostly accounted for in the “embedded” water that’s needed to produce everyday products—the food we eat and what it took to grow or rear it, the clothes we wear, or the energy we use.


Prompt: Industry is heavily reliant on water, and demand is expected to increase 400 percent by 2050. The textile industry in particular is a huge consumer and polluter of water: it takes 1083 gallons of water to produce one cotton t-shirt, twenty-six of which are needed for dyeing alone. Overall, textile dyeing uses enough water to fill 2 million Olympic swimming pools every year—a number that will continue to grow as more people buy more clothes and wear them for less time.

Response: We live on one planet with finite resources and yet our mode of industrial production has managed to mimic the nightmare scenario of a devastating E.coli megaplanet. How might we transform the scale of industry to conform to its place in nature? How can we make technology grow ecologically rather than exponentially? As a biologist (CA) and designer (NAC), we’ve approached the challenge posed by the need to scale technology within the bounds of nature by designing biology itself as the technology. Natsai’s recent work, for example, tries to minimize the amount of water used in textile dyeing, as well as the amount of pollution it produces. By growing a bacteria called Streptomyces coelicolor—a soil microbe that naturally produces pigmented molecules—directly on a textile, a colorfast pink, purple, or blue finish can be achieved without the use of toxic chemical additives. To dye a t-shirt using this method requires just 200 milliliters of water, 500 times less than current industrial techniques.


Prompt: In this way, the bacteria becomes the coauthor in the creation of a new textile design. The designer no longer specifies all the details of the final product, but rather brings together the conditions in which the living process can thrive and create. Adjust the pH, and the color changes from pink to blue. Alter the amount of oxygen or moisture, and the intensity changes. Alter the genetic pathways of the cell, and new colors and conditional patterns can emerge. This notion of co-authorship marks a fundamental paradigm shift for the designer: a new kind of learning to live within one’s means. Rather than the traditional model of specify, procure, and transform, the designer learns to develop outcomes with a living system and within finite biological limits.

Response: Processes like these point towards the possibility for a new era of biologically derived materiality. Rather than an old industrial model based on extracting resources from nature, they allow us to think about how to re-industrialize according to biology’s ability to grow and scale—to extract nature’s logic, harnessing biological processes for human production.


Prompt: Such a re-industrialization might enable new aesthetic and technical opportunities, from microbially defined patterns to liquid leather. We might imagine microbially enhanced homes, e-readers made from specially engineered leaves, or laptops grown from sawdust and programmable fungus. While these offer inspiring visions for a biological future, achieving this future requires more than just biology. New technology is not enough; the surrounding system has to change as well. Dyeing textiles using bacteria may use much less water and do away with toxic effluent, but the economic imperatives that structure the fashion industry threaten to limit this innovation’s impact. The reality that underpins the fashion industry is a “race to the bottom line” business model, calibrated to sell more units no matter the cost—human or environmental.

Response: Moreover, when industry does make use of biological methods, the impact is often destructive. Biological manufacturing forced into a culture of industrialization has led to dire ecological consequences (clearcut forests, feedlots) and will only continue to do so if we adopt biological processes without adopting biological constraints. Scaling biology to meet the needs of 7,589,052,176 people therefore requires us to reconsider and redesign more than just what can be encoded in DNA.


Prompt: From Petri Dish to Planet A single E. coli has a volume of about 0.7 cubic micrometers. More than seven trillion densely packed cells can fit into a teaspoon. Accessing the incomprehensible smallness of the molecular scale requires training, techniques, and tricks to bring the microscopic into focus.

Response: At this laboratory scale, biologists and engineers have designed organisms that can produce medicines, fuels, materials, and chemicals—living, biological sources for all kinds of stuff that today is more likely to come from petrochemical sources. But scaling these processes from the lab into products that people can use has proven challenging. Despite microorganisms’ penchant for exponential growth in the right conditions, adapting these techniques to systems of industrial manufacturing takes much more than placing a cell in a vat and waiting for it to grow.


Prompt: There are the technical challenges posed by the microbes themselves, unwilling to produce what we ask of them. But there are also challenges posed by scaling within the limits of overlapping contexts of ecosystem, economy, society, and planet. Biofuels, for example, are produced from renewable biological sources via bio-industrial processes, but the reality of how land use, government subsidies, fermentation, and the price of oil are entangled in its production mean that the jury is still out on whether biofuels are any better than gasoline when it comes to contributing to climate change.

Response: Technology, as Ursula M. Franklin notes in The Real World of Technology, “entails far more than its individual material components. Technology involves organization, procedures, symbols, new words, equations, and most of all, a mindset.” A deep understanding of DNA, enzymes, and microbes—the individual material components—is necessary to scaling biological technologies. But so too is an understanding of chemical engineering and process development, environmental science and life cycle analysis, sourcing and supply chain management, economics and market dynamics, politics and regulatory governance, sociocultural forces, consumer needs and perception, product design, and marketing.


Prompt: No one person can see across all these scales, from the molecular to the global. We need organizations and language to bridge scales and disciplines, to work together to achieve the necessary mindset of ecologically bounded technology. For us, that has meant learning how to learn from each other: for a designer to learn to see the microscopic scale and grow bacteria in the lab, and for a biologist to learn to see at the scale of social context. Without the tools to collaborate from petri dish to product to system we run the risk of creating a “disruptive” technology that doesn’t disrupt anything.

Response: As biological technologies emerge and scale, we must nurture not only the cells in our petri dishes and fermentation vats, but also the people, organizations, and systems that are necessary to grow a new kind of industry, scaled to our planetary context. A truly radical bio-industrial revolution will see the line between pastoral farmlands, biological foundries, and industrial factories completely blurred. In this new revolution, biology becomes both the creator and constrainer of scale, empowering everyone on the planet to live well—within limits.