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A Modest Proposal

2026-06-29

Would you like to have some real impact on the tech industry? Do you have $100,000 to spend? If you answered “yes” to both questions, ask software engineering researchers (the kinds of people who participated in It Will Never Work in Theory) to design a study that companies could run internally to measure the impact that genAI adoption by programmers is having on business outcomes. Spend $50K to get expert reviews from both practitioners and (other) researchers, publish all of the proposals with the reviews, and award prizes of $25K, $15K, and $10K to the three best proposals. (If you really want to have an impact, do this in two rounds so that participants can hybridize their best ideas.)

$100K feels like a lot of money…

Really? Compared to what you’re spending on tokens?

Does anybody actually know how to measure genAI’s impact?

It’ll be interesting to find out. (After all, “yes” and “no” are equally interesting answers.)

Will people write decent proposals for just a few thousand dollars?

No, but they’ll do it for the attention, and for the chance to be involved in running the study if their proposal is a winner.

I’m interested—how do I get the ball rolling?

Let’s talk.

Three Outlines

2026-06-29

I’m currently making a few last changes to the third book in this series and trying to find an agent who will handle them. If you have middle-graders who would be interested in reading them and giving me feedback, please give me a shout.

Maddy Roo

Maddy Roo takes place in a world of anthropomorphic animals and patchwork robots. Its protagonist, Maddy, is a 12-year-old kangaroo whose younger sister, Sindy, is a “throwback” with no fur, scales, or tail. Their father was kidnapped by a raiding band of robots two years before the story opens; they and their mother have struggled to make ends meet since then.

While Maddy is out one evening with a goat boy named Gumption they rescue a damaged robot from a stream. Its regulator has been broken, which allows it to reveal that another raid is about to take place. Maddy and Gumption rush back to town to warn everyone. The raiders are driven off, but not before taking Maddy’s sister and two other children.

Maddy teams up with the rescued robot, Dockety, to get her sister back. The pair manage to catch up with the raiders and free the prisoners, but in the confusion that follows, Maddy, Sindy, and Dockety are stranded in a dangerous swamp called the Mire. They take refuge in an abandoned bunker, only to discover that it is the lair of a mad robot named Patient in Darkness, who is responsible for the raiding parties.

The trio escapes by bolting a flying suit onto Dockety and get back to town moments ahead of the raid. In the aftermath of the battle that follows, Maddy realizes that she knows how to free the bots that Patient has enslaved. She uses the flying suit to return to the bunker and break Patient’s control. As the story ends, Dockety reveals that Maddy’s father is still alive and is being held prisoner in the bot city of Heck.

In Heck

In Heck picks up several months after Maddy Roo. Dockety’s community of free bots has settled just outside Rusty Bridge, and Dockety has confirmed that Maddy’s father being held in the bot city of Heck. When Sindy accidentally activates a visiting Operator’s tech during a school demonstration and badly burns Special Leaf, the Operators insist on taking her to their headquarters in Sandy Bend, even though Special Leaf warns Maddy not to let them.

Maddy and Gumption stow away in the Operators’ wagon, but a rogue flying bot kidnaps Maddy mid-journey and delivers her to the mad bot Patient in Darkness. Patient claims the Operators in league with Central (the AI that controls Heck) which plans to exploit Sindy’s ability to activate Maker technology. Maddy escapes with a discombobulator that hides her from machines and a small cleaning bot she names Mouse, makes her way to Heck, and watches helplessly as the Operators hand Sindy over to Central’s bots.

Meanwhile, Gumption and Dockety seek help from a community of free bots in the forest. A reclusive bot called the Tailor disguises Gumption as a machine, and they reluctantly join forces with Patient. Inside Heck, Maddy finds her father, but is captured and placed in a virtual reality. There, Central reveals it is trapped by its own programming and longs to end.

The climax takes place in Central’s laboratory. Gumption’s disguise lets him briefly command Central’s bots, but Patient seizes control of Central through Sindy’s network connection. Sindy defeats Patient, Thoughtful turns on Special Blazes to save Sindy, and Dockety is nearly destroyed shielding Sindy from harm. The group escapes Heck with Maddy’s father and a handful of other prisoners. As the story ends Papa Roo dreamily announces that the Makers are awake and returning.

The Makers Return

The Makers Return picks up several months after In Heck. Special Leaf has died and left his house and his collection of ancient tech to Sindy. With Maddy and Gumption away in Sandy Bend, she is struggling to find her place in Rusty Bridge. She discovers a communicator that connects her to Violet, a young human girl aboard a failing spaceship called the Ark that has lost contact with Central and is running out of fuel. The ship’s commander, Captain Leung, decides it is time to return to the planet.

Special Blazes returns to Rusty Bridge with a new partner just as the Ark crash-lands in the swamp, where it is seized by a tentacled bot first encountered in Maddy Roo. Sindy uses her abilities to command the bot to release the ship, but a second attack creates chaos. Captain Leung seizes Sindy and, with Violet, flees in a shuttle. When the shuttle is forced down near an abandoned bunker, Patient in Darkness is waiting for them. The mad bot captures the group and uses a neural cap to read Captain Leung’s memories, confirming that the Makers have truly returned.

Violet discovers that Patient plans to use the Makers’ combat bots to destroy the ark. She escapes with Sindy and Mouse, and lead Patient (in a giant new body) back to Rusty Bridge. There, Violet wields a remote-control glove from Special Leaf’s hidden cache to disable Patient’s forces, and Mouse convinces the swamp creature to drag Patient under the water for good. In the conclusion, we see Violet and other children from the Ark settling into Rusty Bridge as Captain Leung calls the other arks home.

Years Too Late

2026-06-26

I’ve been unemployed for eight months now, and haven’t written as much as I thought I would. Middle-aged angst is one reason, but another is the realization that most of the projects I was thinking of doing are solving yesterday’s problems. I have a long history of doing this:

• The JavaScript and Python versions of Software Design by Example are the books I needed in the 2000s when I was teaching undergraduate courses at the University of Toronto. I’m proud of them, but they have essentially found no readers.

• Similarly, Research Software Engineering with Python would have been really useful if it had appeared in 2012 or 2013. By the time it came out in 2021, most of what it said was already online in a hundred places (thanks in part to Software Carpentry).

• By the time we shut down It Will Never Work in Theory I had learned enough to write an introductory textbook on software engineering that would show readers what we actually know about software development and (just as importantly) why we believe it’s true. I think that a course like that would have prepared people to tell whether AI is making them more productive or not, but as I wrote six years ago, the people who teach undergrad SE courses don’t seem to be interested in changing the curriculum.

Which brings me to the projects I’ve been noodling with since November: workshops on organizational change, project closure, and managing research software projects, and tutorials on debugging, Gleam, and a few things programmers ought to know about how society actually works. According to page views on Plausible, none of these have had more than a couple of dozen viewers a month, and when I ask people for feedback, I hear crickets. As someone who believes we ought to teach young programmers to pay attention to evidence, it’s hard for me to ignore these signals; as someone who has written several books that (to first order) nobody read, it feels foolish to do so.

So I’ve been trying to write fiction instead, which has its own frustrations. Publishers are drowning under AI slop, so most won’t accept unagented submissions any longer, but agents are drowning as well. (There is also the fact that my fiction might not be as good as I think it is: feel free to judge for yourself.) I’ve tried self-publishing a couple of times in the past; the results made sales of my technical books look stellar.

Which leaves me looking at two half-finished YA novels and a pile of non-fiction essays, and wondering if any of it is worth any more time. A friend suggested that I put it all aside and devote myself to Toronto Nature Stewards or some other volunteer work for a few months, if only to get off the screen and meet some new people. There’s also an election coming up; city councillors are always grateful for IT help, and working on a winning campaign has been on my bucket list for over thirty years. Right now, though, I’m going to take another look at the outline for one of those stories and hope that inspiration strikes.

Time for another cup of tea. If you came in peace, be welcome.

AI Happens

2026-06-18

A lot of people are afraid that AI is going to take their jobs. That fear is legitimate: it’s what happened in agriculture when mechanization arrived in the nineteenth century, to craft manufacturing when factory automation took over, and to office work when computers eliminated most routine clerical jobs. New work appeared, but it wasn’t the same work or in the same places, and it wasn’t for everyone who needed it. I think understanding that history is essential to understanding what’s happening with AI.

What AI Is, and What Automation Does

Large language models are trained on enormous quantities of text and code, almost all of which was produced by people who weren’t paid and didn’t consent. LLMs generate statistically plausible outputs: they don’t understand what they’re saying, and can’t verify that their output is accurate or distinguish confident nonsense from correct reasoning because they don’t reason [Torres2024].

In December 2023, the New York Times filed suit against OpenAI and Microsoft, arguing that training a commercial AI system on millions of copyrighted articles without permission constituted infringement. It was the first major legal test of that question, and courts in multiple countries are working through similar cases. As described earlier, intellectual property law has always been an arena where the better-resourced party has a structural advantage. Whatever precedents emerge from these cases will reflect who could afford to pursue them to conclusion, not some Platonic ideal of “right”.

None of this makes AI unusual as a technology. Manufacturing employed 19.4 million workers in the US in 1979. By 2023 that had dropped to 12.8 million. The jobs that replaced the ones that automated or were shipped overseas were often in different sectors or different regions, and almost always lower paid. Towns built around steel mills or textile factories didn’t reinvent themselves as technology hubs: they lost population, services, and tax base simultaneously, and many have not recovered.

The economist Daron Acemoglu estimates that roughly half of the increase in US income inequality since 1980 can be attributed to automation that systematically replaced mid-wage workers with machinery and software [Acemoglu2023]. The gains from automation go to whoever owns the tools, while the cost of retraining, years of lower wages, and the disruption of moving somewhere else fall on the workers who are displaced. Every previous wave of automation distributed those costs unfairly as well, not because it had to, but because the people who owned the technology had more political power than the people displaced by it.

The Demand Problem

The economic structure of AI displacement creates a specific problem that economists Brett Hemenway Falk and Gerry Tsoukalas call “the AI layoff trap” [HemenwayFalk2026]. In competitive markets, an automating firm captures the full cost savings from replacing workers but bears only a fraction of the resulting demand destruction. In a market with twenty competitors, each firm absorbs one-twentieth of the demand it destroys; the rest falls on rivals. Every firm therefore has a rational-as-in-psychopathic incentive to automate beyond the socially optimal level, because the gain from cutting labor costs outweighs the diffuse shared consequence of eliminating consumer spending.

AI worsens this: wider productivity gains accelerate the race toward a shrinking market. Ironically, Henry Ford (no friend to workers) understood the opposite logic: his employees needed to earn enough to buy his cars. The AI economy is eliminating the workers and expecting the cars to keep selling [McGrann2026].

Sometimes the layoffs happen before anyone checks whether the technology can do the job. Acemoglu’s term for this is “excessive automation”: using AI to eliminate jobs without generating meaningfully lower production costs, while imposing substantial social costs. When Block’s Jack Dorsey laid off nearly half his workforce in March 2025, citing AI coding agents, investors responded by boosting Block’s stock price by twenty-five percent. The market rewarded the elimination of human labor with an immediate transfer of value to shareholders, regardless of whether the AI actually performed the eliminated work.

In the Long Run

Anne Case and Angus Deaton tracked what happened to communities when manufacturing employment disappeared. The answers were grim: rising rates of suicide, drug overdose, and alcoholic liver disease all increased among people who had lost their economic function [Case2021,Suzman2021]. The mechanism was not only poverty but the loss of purpose, social status, and a perceived future. As noted above, communities organized around industries that left did not quietly transform into something else.

AI tools are also degrading the workers they are supposed to help. Anthropic’s own internal research found that junior engineers who relied heavily on AI coding agents understood their work significantly less when tested afterward, even though they completed tasks at roughly the same speed as those who did not. The retraining argument assumes people can develop new skills to stay relevant. The evidence suggests that the tools accelerating displacement are simultaneously eroding the capacity for skill development.

What makes me really angry is that the research underlying this technology was publicly funded. The mathematical advances, training methods, and semiconductors were developed through universities, DARPA, and national laboratories, but private companies captured the reward. As Mazzucato has argued, invention has become an engine of rent extraction rather than value creation [Mazzucato2013].

We’re now speed-running that process. By the first three quarters of 2025, AI-related investments accounted for roughly thirty-nine percent of US economic growth, giving the federal government a vested interest in sustaining the boom. The interventions that economists have identified, such public ownership stakes in AI infrastructure, aggressive antitrust enforcement, and a tax on automated labor, are what people in public health call “abstinence solutions”: they would work if people actually implemented them, but we know that’s not going to happen.

The Business Model and the IP Problem

AI services are currently cheap or free, but that can’t last. OpenAI lost approximately $5 billion in 2024 providing cheap API access. The cheap phase exists because companies are burning investor capital to capture market share and deprive competitors of users. This is enshittification all over again: attract users with artificially low prices, build dependencies, then raise prices once alternatives have been squeezed out. The useful, affordable version of these tools will not survive for long, and the developers, writers, and companies that build workflows around them during the subsidized period will pay for it later.

The intellectual property question adds a separate layer of instability to the whole enterprise. Writers, artists, musicians, and software developers whose work was ingested to train commercial AI systems received neither payment nor credit for that contribution. Whether this constitutes infringement, fair use, or something else entirely is actively contested in courts across multiple jurisdictions. The outcomes will depend partly on how judges read copyright law and partly on which side has the resources to sustain litigation that may take a decade to resolve. The largest AI companies have substantially more resources than the individual creators suing them.

The Standard Playbook

Major AI companies have not waited for regulators to define rules that might constrain them. They have placed former employees and allies in regulatory positions and submitted their own proposed frameworks to legislative consultations. For example, when the European Union was developing its AI Act, Anthropic, Google, and OpenAI all submitted proposals that would have exempted their most powerful models from the Act’s strictest requirements.

AI laboratories have also funded their own safety research and publicized favorable results. Critics of AI development have been characterized as alarmists, and documented harms have been described as edge cases. When OpenAI’s safety team resigned in 2024, several members stated that commercial considerations had systematically overridden safety commitments. This sequence—fund your own science, frame independent critics as emotional rather than analytical, and describe any harm as an unfortunate anomaly—is the same one used by tobacco companies and the producers of leaded gasoline.

The reframing of displacement as individual opportunity is equally familiar. The slogan “AI won’t replace you; someone using AI will” shifts the burden of adaptation entirely onto workers and treats the costs of corporate automation as a personal problem requiring a personal solution. This is the passion principle applied to survival: workers are told to reskill and stay relevant, rather than that the economy owes them any compensation for a transition they did not choose. The same framing accompanied every previous major automation wave.

What Collective Action Has Achieved

In 2023, the Writers Guild of America struck for five months over issues that included AI. When the strike ended, the WGA had won explicit contract language: AI cannot write or rewrite scripts, and scripts cannot be used to train AI systems. The Screen Actors Guild reached a parallel agreement that included restrictions on the digital replication of performers’ likenesses without ongoing consent [Kelly2022]. These victories established enforceable contractual limits on what employers could do with AI—limits that individual workers negotiating alone could never have secured. The lesson is not specific to Hollywood: wherever workers have collective bargaining rights, they can negotiate from a position of strength. Professional associations, open-source communities, and standards bodies can create analogous leverage in sectors where formal unions are absent or weak.

Regulation has also moved faster than the industry claims is possible. The EU AI Act requires transparency for high-risk systems, mandates human oversight for consequential automated decisions, and bans specific applications outright. Canada, Brazil, South Korea, and the United Kingdom all have AI governance frameworks in development. Before the EU’s General Data Protection Regulation took effect in 2018, industry associations described it as “unworkable” and predicted that it would destroy European tech competitiveness. By 2024 it had generated approximately $4 billion in fines and had changed how companies worldwide handle personal data, including companies with no European operations that simply chose to comply rather than maintain two systems. The argument that AI regulation will destroy innovation has been made about every major technology regulation in living memory, and has been wrong every time.

How Change Happens

2026-06-17

What Has Actually Worked

Alternatives to the dysfunctions described in this series of post exist. Ranked-choice voting in Ireland, Australia, New Zealand, and more than a dozen US cities has not produced chaos: it has produced legislatures that more closely reflect what voters actually want. Independent redistricting commissions have measurably reduced partisan gerrymandering in Arizona, California, and Michigan, where independent bodies now draw district lines rather than the legislators who benefit from them. New York City’s public matching funds for small donations have shifted the incentive structure for candidates, making it possible to run a competitive campaign on small contributions rather than depending on a handful of major donors. Broad constitutional reform through sustained public participation succeeded in Iceland following the 2008 financial crisis.

Campaigns that engaged the active participation of roughly 3.5 percent of a population have been sufficient to force political change in case after case. Electoral organizing, legal challenges, constitutional campaigns, and redistricting advocacy have all worked when that threshold of organized, sustained pressure was reached. The rich and powerful will always resist; while the specific tools differ, sustained, organized pressure wins time after time [Young2024].

A Paradise Built in Hell

On the morning of December 6, 1917, a French munitions ship collided with a Norwegian vessel in Halifax Harbour, Nova Scotia. The resulting explosion killed nearly two thousand people and flattened the north end of the city. It was the largest human-made explosion before the nuclear age.

Within hours, survivors were pulling strangers from rubble, improvising hospitals in churches and railway stations, and sharing food with people they had never met. The next day a blizzard arrived. Residents of Truro, two hours away by train, loaded relief supplies and medical teams before anyone had formally organized them. People came from across eastern Canada and the northeastern United States, not because anyone had issued orders, but because other people needed help.

This is not the story most people expect. The version of human nature embedded in popular culture and reproduced in disaster media coverage is that when things fall apart, so do people. Civilization is a thin crust over barbarity: scratch the surface and you get looting, assault, and the strong preying on the weak. This story is wrong in almost every particular, but it keeps being told because it serves purposes that have nothing to do with accuracy.

The sociologist E.L. Quarantelli spent decades studying disasters and came to a conclusion that surprised many people: panic and antisocial behavior are the exception, not the rule. Communities typically show increases in prosocial behavior: strangers help each other, crime rates generally fall, and people who were barely acquaintances briefly become something like a community.

Rebecca Solnit documented this pattern across a century of catastrophes. Her case studies, including the 1906 San Francisco earthquake, the 1917 Halifax explosion, the 1985 Mexico City earthquake, the September 11 attacks in New York, and Hurricane Katrina in New Orleans, illustrate Quarantelli’s findings. Disasters reveal a capacity for mutual aid that is usually suppressed by the atomization of modern consumer society.

Hurricane Katrina in 2005 produced the most extensively documented divergence between media narrative and documented reality in modern history. In the days after the storm, major news organizations reported roving gangs in the Superdome, mass rape, and snipers firing at rescue helicopters. Subsequent investigation found that the reported gang violence did not happen, the murder rate in the city did not spike, and most of the “looting” was people taking food and water to survive.

But these lies had consequences. Hospitals delayed evacuating critically ill patients while waiting for military escorts. Trucks carrying food and water were turned back from routes deemed dangerous when they weren’t. A group of survivors trying to walk across the Crescent City Connection bridge to reach Gretna, where they had been told buses were waiting, were turned back at gunpoint by police who said they were keeping their community safe. The fiction of social breakdown caused deaths that the storm itself had not.

Solnit has a name for what happened in New Orleans: elite panic. Ordinary people in a disaster tend to behave with remarkable generosity and calm, but authorities and elites tend to panic—not about the disaster, but about the public. Since they believe that the social order that keeps them on top is only held together by the threat of force, a disaster that removes their ability to enforce their rules looks like the end of civilization.

The gap between what happens in disasters and what gets reported is also explained by what counts as news. Editors make decisions about what to show based on what will attract attention, and dramatic conflict attracts more attention than organized mutual aid. The result is systematic selection bias in disaster coverage. If the media consistently describes human nature as more violent and more selfish than it actually is, people are pre-conditioned to believe that cooperation is unlikely, which makes them less likely to cooperate. Just as advertising can manufacture demand, biased reporting can manufacture mistrust, and in doing so, hurt us all [Quarantelli1998,Solnit2009,Tierney2006].

The Ozone Hole That Closed

In 1974, two chemists at the University of California published a paper predicting that chlorofluorocarbons (CFCs) would destroy the ozone layer in the stratosphere that shields Earth from ultraviolet radiation. CFCs were used as propellants in aerosol cans and refrigerants in air conditioners, and while the paper’s authors didn’t yet have a hole to point to, they had atmospheric chemistry on their side.

The chemical industry’s response was to fund counter-research, hire lobbyists, and describe the scientists as alarmists whose work was too speculative to justify regulatory action. This was the same playbook that the tobacco industry had been running for two decades, and for a while it worked. The Alliance for Responsible CFC Policy, a trade group representing the manufacturers, argued that the science was uncertain. Industry representatives testified before Congress that banning CFCs would cost hundreds of thousands of jobs and devastate the American economy.

Du Pont, which held a large share of the CFC market, said in 1975 that it would stop making CFCs only if a worldwide scientific consensus emerged and the appropriate regulatory bodies took action. This was not a promise to act; it was a description of conditions the company presumably believed would never be met.

Eleven years later, in 1985, a team from the British Antarctic Survey reported a massive and growing thinning of the ozone layer over Antarctica every southern spring. Their data was so far outside expected ranges that they initially assumed their instruments were broken. NASA confirmed the finding using satellite data that, embarrassingly, had been sitting in archived files for years after automated quality-control software had flagged the anomalous readings as errors [Roan1989].

Industry resistance collapsed in just two years, and the Montreal Protocol was signed in 1987. The protocol’s design explains why it worked when so many other environmental agreements have not. It set binding phase-out schedules for ozone-depleting substances, with different timelines for developed and developing countries. It established trade sanctions against non-signatories, which meant that countries outside the agreement faced economic costs for staying out. And it created the Multilateral Fund, which transferred technology and money from wealthy countries to developing ones to help them adopt CFC alternatives. This last element is the one that gets least attention and does the most work.

The protocol’s structure gave it leverage that most international agreements lack. A country that refused to sign could not import controlled substances from signatory countries and could not export products made with those substances to them. By the early 1990s, enough of the global economy was covered by the agreement that staying outside it became genuinely costly.

Du Pont, which had spent years arguing that alternatives to CFCs were technically impossible, announced shortly after the protocol was signed that it had developed workable substitutes and would accelerate their commercialization. What had been technically impossible became technically straightforward once the regulatory framework made the old product unmarketable.

The substitutes developed to replace CFCs were hydrofluorocarbons—HFCs. They did not destroy the ozone layer. They did, however, turn out to be extremely potent greenhouse gases, some of them thousands of times more warming per molecule than carbon dioxide. In switching from one problem to another, the world had traded an acute crisis for a contribution to a chronic one.

The Kigali Amendment to the Montreal Protocol, adopted in Rwanda in 2016, addressed this. It added HFCs to the list of controlled substances and set phase-down schedules for them as well. Developed countries agreed to begin reductions by 2019; most developing countries by 2024 or 2028, with a small number of the hottest-climate countries, including India and Pakistan, given until 2032. The amendment was negotiated under the same structure as the original protocol, with the same Multilateral Fund available to support transitions. Climate scientists estimated at the time that full implementation of the Kigali Amendment would avoid up to 0.4 degrees Celsius of warming by 2100.

This is not is a story about individual consumers making better choices. Millions of people did not read scientific papers and switch to pump-action hairspray. The mechanism was a binding international agreement with differentiated obligations, a technology transfer fund, and trade sanctions against non-participants.

The lesson for climate change shouldn’t need to be spelled out, yet it rarely appears in public discussions. Renewable energy investment, corporate sustainability pledges, and carbon pricing mechanisms will all help, but the decisive ingredient for the ozone layer was a binding agreement with teeth. Similarly, if we want to mitigate the cognitive pollution caused by social media, country-by-country age verification isn’t going to make a difference [Parson2003].

Land to the Tiller

In 1947, the United States government did something that its own politicians would have called socialism if anyone else had done it. Under American military occupation, Japan’s agricultural land was seized from landlords and sold to the tenant farmers who had been working it, at prices set well below market value, paid in bonds that inflation promptly turned into confetti. This was expropriation, and it worked.

The Cold War was the reason. American planners in Tokyo feared that rural poverty and landlord domination were exactly the conditions in which communist movements flourished. They had watched what happened in China and did not want a repeat, so they did what they would never have considered at home: they redistributed productive assets from the wealthy to the poor on a massive scale and called it democratization.

Between 1947 and 1950, roughly thirty percent of Japan’s farmland changed hands under the land reform program. Landlords who had lived off tenant rents for generations suddenly held bonds whose real value was eaten away month by month, while the tenants who had always done the work owned the fields. The landlord class as an economic force essentially ceased to exist. What replaced it was a rural middle class of owner-farmers. In the following decades, those farmers’ children moved to the cities and provided the workforce for Japan’s industrial expansion. The land reform did not just change who owned the fields; it restructured the society that would industrialize in the 1950s and 1960s [Dreze2013,Studwell2013].

South Korea and Taiwan followed the same template, for the same reasons, at almost exactly the same time. In both places, American advisors pushed land reform as a counter to communist land redistribution programs that were mobilizing peasant populations elsewhere in Asia. In South Korea, the Land Reform Act of 1950 capped landholdings and required excess land to be sold to the state for redistribution to tenant farmers. In Taiwan, the program between 1949 and 1953 transferred land from Taiwanese landlords to the tenant farmers who cultivated it. The compensation paid to landlords in both countries was structured in ways that made delay expensive: bonds whose value eroded, or equity in state enterprises whose worth depended on economic policies the landlords no longer controlled.

The design was intentional. Reform administrators understood that the landlord class would use any instrument available to reverse the transfer, and they structured the compensation to reduce the resources available for that reversal. This was not incidental: the land reforms created the conditions for the subsequent industrial policies to succeed, because the rural population had both the stability and the incentive to participate in markets rather than spending their energy surviving extraction.

The Japanese, Korean, and Taiwanese cases all share a feature that is easy to overlook: the reforms were imposed from outside, and so were insulated from the normal political power of the landlord class. This raises an uncomfortable question about whether the reforms could have happened through domestic democratic politics. The state of Kerala, in southern India, provides an answer.

Kerala’s land reform story begins with electoral politics rather than military occupation. The Communist Party of India won state elections in Kerala in 1957 on a platform that included land reform, and despite being dismissed from power by the central government before completing its program, it returned to power and passed the Kerala Land Reforms Act in 1969. The legislation abolished tenancy arrangements that had kept agricultural laborers in conditions of near-permanent dependency, placed ceilings on landholdings, and required excess land to be redistributed. Landlords resisted, courts were used to delay implementation, and the process took years to work through, but it worked.

The Kerala case is important because it demonstrates that land reform can happen through democratic elections in a country where the landlords have full political rights and access to courts and legal challenges. Landlords resisted energetically, but the political organization of tenant farmers and agricultural laborers was strong enough and persistent enough to sustain reform across multiple election cycles and through sustained legal obstruction.

What makes Kerala remarkable is what happened afterward. By the 1990s the state had achieved literacy rates, life expectancy, and infant mortality figures that compared favorably not just to other Indian states but to countries with far higher per-capita incomes. Land reform broke the power of a class that had used political dominance to block public investment in health and education; once that class’s power was broken, public services became possible.

The argument made against land reform in all of these cases was that it would destroy productivity, undermine investment incentives, and leave everyone worse off. Big tech makes the same arguments today about proposals to democratize social media and break up virtual monopolies. There is no reason to believe the outcomes would be different [Studwell2013].

Conclusion

These essays have described how power is structured, how harm is produced and obscured, who bears the costs, and how regulatory and political contests have unfolded in other industries. This final lesson asks what the historical record shows about how change actually happens in documented cases rather than in theory. The answer is consistent across domains and largely unwelcome to people who prefer to change the world through individual choices or technical solutions: change happens when organized groups apply sustained economic and political pressure over time, and it rarely happens any other way. The record also shows that nonviolent campaigns have historically been more successful than violent ones, and that the reasons why are structural and replicable.

The dominant popular narrative about social change centers on individuals: Rosa Parks refused to give up her seat and the Civil Rights Movement was born. This narrative is factually wrong and strategically disabling. Rosa Parks was the secretary of the Montgomery chapter of the NAACP and had recently attended the Highlander Folk School, a training center for labor and civil rights organizers. The Montgomery Bus Boycott that followed her arrest was organized by the Montgomery Improvement Association, coordinated carpools across a city for over a year, and was sustained by the labor of hundreds of people whose names are not remembered. The choice of Parks as the plaintiff in the subsequent legal case was deliberate: other potential plaintiffs had been rejected as less strategically suitable. This is what organized political campaigns look like. The reduction of that campaign to one person’s spontaneous act of courage makes it both more inspiring and less useful as a model [Beckerman2022].

The historical record of successful social change campaigns shows consistent structural features that cut across very different political contexts. Indian independence was achieved through a disciplined mass movement that combined civil disobedience, economic disruption, legal challenge, and international publicity over decades. Polish Solidarity built an independent trade union into a national opposition movement that eventually outlasted the communist state, sustained through martial law and repression by organizational capacity and international support. The South African anti-apartheid campaign combined internal mass action with an international sanctions and divestment campaign that imposed economic costs the apartheid government could not absorb indefinitely. The British suffragette movement used tactics ranging from petitioning and public speaking to window-smashing, arson, and hunger strikes, and it succeeded only after the combination of sustained pressure and the changed political calculus produced by women’s wartime labor made continued denial of the franchise politically untenable. These campaigns differ in tactics, duration, context, and outcome. What they share is organizational discipline, sustained commitment across setbacks, and an understanding of where the economic and political pressure points lay [Chenoweth2011,Lakey2018].

The most rigorous quantitative analysis of this question is Erica Chenoweth and Maria Stephan’s study of 323 resistance campaigns between 1900 and 2006. Their finding is that nonviolent campaigns succeeded roughly twice as often as violent ones, and that the threshold for success was consistent: campaigns that engaged the active participation of roughly 3.5 percent of the population did not fail. The mechanism is not mysterious. Nonviolent campaigns can recruit from a broader population, including people who will not take up arms but will march, boycott, strike, or withdraw labor. Broader participation creates broader legitimacy and makes it harder for the state to frame repression as protecting order rather than suppressing dissent. The 3.5 percent figure is not a guarantee; it describes a historical pattern. But it is a more useful starting point than the assumption that popular majorities produce change automatically.

Economic disruption is the mechanism that connects organized pressure to actual policy change. Boycotts raise the cost of doing business with a target. Strikes remove the labor on which production depends, and divestment campaigns raise the cost of capital for targeted firms or governments and create reputational pressure on institutional investors. The Montgomery Bus Boycott worked because it destroyed the bus company’s revenue from its Black ridership. The South African divestment campaign worked because it raised the cost of the apartheid state’s international borrowing and created political problems for governments whose pension funds held South African assets. In each case the mechanism was economic: the people in power faced a cost-benefit calculation that changed. Moral suasion may have affected some individuals. It did not change the structural calculation that drove policy.

Moral arguments have a poor track record as the primary lever of social change, and this fact is frequently misunderstood. It is not that moral arguments are irrelevant: they build coalitions, provide the normative framework that justifies what a movement is asking for, and affect the willingness of potential participants to accept personal costs. But moral arguments addressed to those in power, without the economic or political pressure that makes their rejection costly, consistently fail. Slaveholders did not free enslaved people because they were persuaded that slavery was wrong. The tobacco industry did not voluntarily stop marketing cigarettes to children because public health advocates published articles about harm. Corporate privacy practices do not change because researchers demonstrate the extent of surveillance. What changes the behavior of those who benefit from a harmful arrangement is when not changing becomes more costly than changing, and that calculation is economic and political.

The word “political” is consistently used disparaginly in tech culture, as if politics were something that happens elsewhere and that a well-run technical organization can avoid. Politics is the process of making collective decisions in the absence of agreement on goals. Every decision about which features to build, which users to prioritize, and which harms to accept does this. The refusal to engage with questions framed as political does not remove politics from the process; it delegates those decisions to whoever is willing to engage. Refusing to vote is a political act. Refusing to join a union is a political act. Choosing to work on a product without asking who it will harm is a political act. The only question is whether the political choices being made are made consciously and with an understanding of their consequences [Young2024,Chenoweth2011].

Regulation Works

2026-06-16

Cognitive Pollution

Engineers learn to reason about direct, traceable failures: a faulty valve leads to a boiler explosion or a bug crashes a program. This model frames harm as rare, dramatic, and attributable, but the most serious damage caused by industry hasn’t actually worked this way. Instead, it has been diffuse, cumulative, slow to emerge, and difficult to attribute to any single decision or actor.

When leaded gasoline lowered the IQs of an entire generation of children, no single tank of fuel caused a measurable injury. Similarly, no particular cigarette is responsible for any particular cancer death. The harm is real and massive, but it was distributed across millions of exposures, tens of millions of people, and decades, so those responsible didn’t meet the legal requirement of direct and proximate cause.

This pattern is no longer confined to physical toxins. Social media platforms optimized for engagement produce radicalization and depression as a byproduct. The harm is diffuse: no single recommendation causes a school shooting or an act of genocide. The long, probabilistic causal chain makes it difficult to assign fault and therefore difficult to regulate.

The term cognitive pollution is increasingly used to describe this. As with other forms of pollution, it is proving difficult to regulate, and tech companies have every incentive to maintain that difficulty. After all, as long as harm cannot be attributed to them, they can externalize its cost onto the people who absorb it.

The tobacco industry did not accidentally produce uncertainty about the link between smoking and cancer. It funded research specifically intended to produce uncertainty, identified scientists willing to dispute the consensus, and maintained that effort for decades after the science was settled. The same pattern appears in the history of leaded gasoline, asbestos, oxycontin, and now social media and AI [Oreskes2010,Michaels2008].

Civil and chemical engineers are now taught about pollution, not because the profession had a crisis of conscience, but because society decided over the course of many decades and through many court cases to hold polluters liable for harm. Noise pollution, and now light pollution, are retracing that history, and I think that if we take mental health as seriously as physical health, it’s inevitable that we will start to hold companies accountable for the mental suffering they cause.

If we frame harm as rare, dramatic, and attributable, responsible engineering means avoiding the specific decision that produces attributable failures. Under the pollution model, on the other hand, responsible engineers are accountable for long-term cumulative effects. A few recent court judgments in the United States may show that this shift is finally happening, but they will undoubtedly be contested, and as the overturn of Roe v. Wade shows, precedent isn’t enough of a guarantee. Legislation that holds tech companies responsible for the damage their products do to users’ mental health can come sooner, will be far more robust, and will have more impact than pious gestures like banning young people from using social media [Perrow1999,Singer2023].

How Tobacco Was Tamed

In 1950, Hill and Doll published a landmark paper in the British Medical Journal. They had interviewed hundreds of lung cancer patients and healthy controls in London hospitals and found that people with lung cancer smoked cigarettes at dramatically higher rates than people without it. The conclusion was not ambiguous; the tobacco industry’s response was a masterclass in how to make clear things seem murky.

Within four years, American cigarette manufacturers had formed the Tobacco Industry Research Committee, later renamed the Council for Tobacco Research. They hired scientists, funded studies, placed ad in newspapers, and issued press releases arguing that the evidence was inconclusive and that more research was needed. Iinternal documents that became public proved that they knew these were lies, and that tobacco company executives understood the health risks long before the public did. Their goal wasn’t to disprove the science: it was to create enough uncertainty that politicians felt they could not act and smokers felt they could not be sure.

This strategy was later used by leaded gasoline producers against evidence linking lead to cognitive damage in children, and by fossil fuel companies against climate change. It follows a template:

• Fund scientists willing to generate alternative hypotheses, however implausible.
• Insist that correlation is not causation.
• Describe any call for regulation as an attack on personal freedom or scientific integrity.
• Create front organizations with neutral-sounding names that can advocate on your behalf without obvious commercial interest.
• Delay, delay, delay—because delay is profit.

If this sounds familiar, it’s because tech companies that argue they are “just a platform” and not responsible for the content they amplify are doing exactly the same things [Oreskes2010].

Forty Years to an Agreement

The United States Surgeon General issued his landmark report in 1964, fourteen years after Doll and Hill’s paper. The report stated clearly that smoking caused lung cancer, and recommended action. The tobacco industry spent the next thirty-four years fighting a rear-guard action. What eventually forced a reckoning was a combination of factors: litigation by individual plaintiffs and then by state governments, pressure from public health advocates, investigative journalism, congressional hearings, and the slow accumulation of economic costs borne by state Medicaid programs that finally gave states both the motivation and the legal theory to sue.

The key legal move was a shift in how states argued their cases. Rather than proving that smoking had harmed specific individuals, which the industry could defeat by arguing about individual risk tolerance, states sued to recover the cost of treating sick smokers. Mississippi was first in 1994, and by 1998 forty-six states had reached the Master Settlement Agreement with the four largest cigarette manufacturers. The companies agreed to pay $206 billion over twenty-five years, restrict marketing to minors, and disband the organizations they had used to manufacture doubt.

It was a victory, but it took 40 years to go from a clear scientific finding to a partial legal resolution, and even then the industry survived and moved into new markets. Other countries responded slowly, but they did respond. For example, consider what happened in Australia. The Labor government introduced legislation requiring plain packaging for all tobacco products: no logos, no distinctive colors, Just the brand name in a standardized font on a drab olive-brown background, surrounded by large graphic health warnings.

The Tobacco Plain Packaging Act passed in 2011 and took effect in December 2012. The industry’s response was immediate and coordinated. British American Tobacco, Philip Morris, and Imperial Tobacco challenged the law in Australia’s High Court, arguing it amounted to an unlawful acquisition of their intellectual property. The High Court rejected this unanimously in August 2012. Then Philip Morris International filed a challenge under an obscure investor protection treaty between Hong Kong and Australia, arguing that plain packaging violated the treaty’s provisions on the fair treatment of foreign investors. That proceeding dragged on for years before an arbitral tribunal dismissed it in 2015 on the grounds that Philip Morris had restructured its Australian operations specifically to gain access to the treaty—a move so transparently opportunistic that even the arbitrators were not impressed.

What eventually made the difference with tobacco was not better science but organized pressure operating through multiple channels simultaneously. Litigation created financial costs large enough to change corporate behavior. Public health campaigns shifted popular attitudes, which in turn changed what politicians felt they could support. Investigative journalism revealed that the industry had known what it denied knowing, which destroyed the credibility of its scientific spokespeople. International organizations like the World Health Organization created a Framework Convention on Tobacco Control that gave national health ministries political cover and legal tools they had previously lacked.

None of these were sufficient on their own: litigation alone produced settlements that left the industry intact, public health campaigns alone had failed for decades against the industry’s advertising budgets, and regulation alone was blocked by industry lobbying. It was the combination that worked, albeit slowly.

The question for anyone wanting to rein in tech companies is therefore how long it takes to build a combination of litigation, regulation, and public pressure strong enough to impose costs on an industry that is wealthy enough and politically connected enough to resist? The tobacco case suggests the answer is measured in decades, and that even then you get a settlement rather than a solution [Brandt2007,Epstein2007].

Unsafe at Any Speed

In the autumn of 1965, a thirty-one-year-old lawyer named Ralph Nader published a book arguing that General Motors was selling cars it knew to be dangerous. Nader focused on the Chevrolet Corvair, whose rear suspension design made it prone to rolling over.

GM’s response was not to fix the car; it was to hire private detectives to dig up dirt on Nader. Investigators followed him, questioned his acquaintances about his sex life and his political views, and arranged for women to approach him in public and try to entrap him. When Nader reported the surveillance, GM’s president was summoned to testify before the United States Senate and had to apologize on national television. The resulting publicity sold more copies of Nader’s book than any advertising campaign could have, and Congress passed the National Traffic and Motor Vehicle Safety Act later that same year [Nader1965].

If you want to understand how industries respond to safety regulation, stories like these are a good place to start. The playbook has not changed much in sixty years: deny the harm, attack the messenger, and insist that the market will handle everything. What history shows is that the market doesn’t handle it: regulation does.

Sweden mandated front seatbelts in new cars in 1959. The evidence for their effectiveness was already solid by then: Nils Bohlin, an engineer at Volvo, had developed the three-point belt the year before, and Volvo had made the patent freely available to every manufacturer in the world. This was not altruism—Volvo wanted to market its cars as safe—but the effect was the same.

The United States didn’t require seatbelts in new cars until 1968, and individual states did’t begin requiring drivers to wear them until the 1980s. (Australia’s Victoria became the first jurisdiction anywhere in the world to require seatbelt use in 1970—a decade before most American states got there.) Airbags, first demonstrated as viable technology in the 1950s, were not required in all new American passenger cars until 1998.

At every step, car makers argued that the requirement was premature, that consumers would choose safety features if they really wanted them, and that regulation would raise costs and kill innovation. At every step, the data showed substantial reductions in deaths after regulations changed. The argument that consumers would choose safety if they valued it was falsified by every study that examined it. When buyers compare cars, they mostly look at price, fuel economy, and styling. They do not systematically seek out crash-test ratings. This is not a character flaw; it is a predictable feature of how people make decisions under uncertainty about low-probability events [Mashaw1990].

Five years before Nader published his book, a pharmacologist at the United States Food and Drug Administration named Frances Kelsey was assigned to review an application for a new drug called thalidomide. The drug had already been approved in West Germany in 1957, where it was prescribed to pregnant women for morning sickness. By 1960, it was being sold in forty-six countries.

Kelsey was troubled by the application’s safety data. The drug affected peripheral nerves in adults, and she wanted to know more about how it crossed the placenta. The manufacturer pressured her repeatedly to approve it. She declined, asking for more data each time.

By 1961, German pediatrician Widukind Lenz and Australian obstetrician William McBride had independently linked thalidomide to severe birth defects. Children born to women who had taken the drug during early pregnancy were born without limbs, or with drastically shortened ones, as well as damage to their eyes, ears, and internal organs. 10,000 children or more were affected in countries where the drug had been approved, but thanks to Kelsey’s skepticism, the United States was spared.

The scandal prompted Congress to pass the Kefauver-Harris Amendment in 1962. Before that amendment, a pharmaceutical company needed to show only that a drug was not demonstrably harmful before selling it. After it, companies had to demonstrate that a drug actually worked, and required that patients give informed consent to experimental treatments.

It is worth thinking about that for a moment. Before 1962, you could sell a drug in the United States without proving it did anything. You just needed to avoid proving that it was immediately lethal. The thalidomide disaster changed that, but only because the disaster had been so catastrophic and so visible that the political cost of inaction became higher than the political cost of regulation.

As noted several times, there’s a pattern here. First comes denial: the evidence is contested, the studies are flawed, the sample sizes are too small. This phase can last for years or decades, especially when the industry funds its own research

Then comes the argument from uncertainty. Even if there is a problem, we do not know enough yet to regulate. More study is needed. Any regulation now would be premature and might target the wrong thing entirely.

Next is the market argument. Consumers will demand safe products if they want them. Competition will drive manufacturers to provide safety. Regulation is unnecessary because market forces will take care of it. This argument fails empirically in case after case because consumers cannot evaluate risks they cannot observe. They cannot detect a placental crossing rate for a sedative. They cannot assess the probability that a suspension design will cause a rollover or compare the structural integrity of crumple zones. Markets aggregate preferences for things people can evaluate; they do not reliably handle latent hazards that require technical expertise and longitudinal data to detect.

Finally, after regulation is imposed, comes acceptance [Hilts2003,Savedoff2012]. The industry discovers that compliance is cheaper than predicted, that safety features are selling points, and that the regulation did not, in fact, destroy the sector. The American auto industry survived seatbelts. The pharmaceutical industry survived the Kefauver-Harris Amendment. If you ask them now, they will tell you that of course they support safety. Ask them about the next proposed regulation, though, and you will hear the same arguments they made about the last one.

When the tech industry tells you that privacy regulation will destroy innovation, or that algorithmic transparency requirements will make AI unworkable, or that holding platforms liable for content will end the internet, you are hearing a very old argument. It has been wrong before. The burden of proof runs in the other direction now: those who claim the market will handle it should be required to explain why this time is different from every other time.

How the Rivers Ran Again

In December 1952, cold air trapped a layer of warm, smoky air close to the ground in London. For four days, a yellow-brown fog of coal soot blanketed the capital, so thick that people could not see their own feet. Buses stopped running because drivers could not see the road. Cattle at the Smithfield show were killed before they could suffer further. People died in their homes, in hospitals, and on the streets.

The British government’s initial response was to deny that the fog had killed anyone; a spokesman suggested that the excess deaths were caused by influenza. At least 4,000 people died in those four days, and researchers later estimated the total at closer to 12,000 once the delayed effects on the elderly and the already-sick were counted. The government finally acknowledged the connection in 1953, under sustained pressure from Members of Parliament whose constituents had died. The Clean Air Act followed in 1956, restricting the burning of coal in domestic hearths and requiring industrial smokestacks to be tall enough to disperse their emissions. Air quality in London improved measurably within years. The great smogs did not return.

The Thames had been in trouble for much longer. By the mid-nineteenth century, the river was an open sewer. The summer of 1858 was so bad that Members of Parliament abandoned their riverside building because the smell made work impossible. Victorian engineers built a sewer system, and things improved somewhat, but a century later the Thames through London was still functionally dead. Oxygen levels in the water were so low that fish could not survive; a survey in the 1950s found none at all in a long stretch of the river.

What changed was not public disgust—Londoners had been disgusted by the Thames for two hundred years. What changed was enforceable law. The overhaul of sewage treatment in the 1960s, driven by statutory requirements, reduced the organic load entering the river. Oxygen levels climbed, and by the early 1970s, fish were beginning to return to parts of the river that had been lifeless within living memory. In 1983, a salmon was caught in the Thames for the first time since the 1820s. That gap—one hundred and sixty years—tells you something about how long environmental damage persists and how long it takes to undo.

On June 22, 1969, the Cuyahoga River in Cleveland, Ohio, caught fire. This sounds dramatic, but it was also the thirteenth time the river had caught fire since 1868. Oil, chemicals, and other industrial waste had been flowing into the Cuyahoga for decades, and fires were not unusual.

What made 1969 different was a photograph. Time magazine published images of the burning river, and the story reached an audience that had never heard of it before. Public outrage followed. That, combined with pressure from environmental advocates who had been working for years without much political traction, contributed directly to the passage of the US Clean Water Act in 1972 and the establishment of the Environmental Protection Agency.

The Cuyahoga itself is now a recreational river—people kayak on it. This is not because Cleveland’s industries suddenly became virtuous; it is because the Clean Water Act made pollution costly in a way that the previous century of moral condemnation had not.

On November 1, 1986, a fire broke out in a Sandoz chemical warehouse in Schweizerhalle, near Basel, Switzerland. Firefighters used water to fight the blaze, and the runoff entered the Rhine, carrying roughly thirty tonnes of pesticides, fungicides, and mercury compounds. The chemical plume moved downstream through Germany and into the Netherlands, killing eels and fish for hundreds of kilometers. In some stretches the river smelled of insecticide. The eel population, already stressed, was devastated. Drinking water intakes along the river had to be shut down.

The catastrophe made the politicians of every country along the Rhine’s banks understand, in a way that years of incrementally worsening data had not, that the river’s problems were shared problems and could only be solved by shared commitments. The Rhine Action Programme, signed by Germany, France, the Netherlands, Luxembourg, and Switzerland, set binding targets for the reduction of pollutants. By the early 1990s, salmon had returned to the Rhine for the first time in decades. The river is now among the most intensively monitored and regulated waterways in the world [Nixon2011].

The Great Smog, the Thames, the Cuyahoga, the Rhine, and Minamata are not stories about environmental virtue. No sudden wave of ecological consciousness swept through London in 1955 or Basel in 1987. What changed in each case was the legal and economic cost of pollution.

Industries and municipalities that had treated rivers and air as free dumps for a century changed their behavior when they faced fines, required upgrades, and liability for damages. The mechanisms differed: criminal penalties in some jurisdictions, civil liability in others, international treaty obligations in others. The result was the same. When it became costly enough, the behavior changed.

Markets do not price externalities without compulsion [Singer2023]. The reasons are simple; what is complicated is the politics of making industries pay for costs they have been externalizing for free. Every major environmental regulation in the twentieth century was fought by the industries it affected, using arguments about economic harm that turned out to be exaggerated and predictions of technological impossibility that turned out to be wrong. The same thing is happening now with attempts to regulate the harm caused by social media, AI, and large tech platforms’ surveillance of everyday life.

Two structural interventions have produced results. The European Union’s Digital Markets Act, which took effect in 2024, requires platforms designated as “gatekeepers” (those with market capitalizations above €75 billion or monthly user bases above 45 million in Europe) to allow interoperability with competing services, to refrain from self-preferencing their own products in search results, and to allow users to uninstall pre-installed software. Fines for non-compliance reach 20% of global revenue. The act is the first regulatory framework designed specifically around the leverage that platform dominance creates, rather than around the consumer prices those platforms charge.

India’s Unified Payments Interface uses a different model: intervene before dominance rather than after. Instead of regulating private platforms that have already achieved lock-in, India built public payment infrastructure that any platform can connect to on equal terms. Google Pay, PhonePe, and Paytm compete on the same rails; none owns the customer relationship—that belongs to the user’s bank account. No single platform can raise fees on the underlying infrastructure because the infrastructure is public. Brazil’s Pix system follows similar principles, as do comparable approaches adopted by central banks in Ghana and Sri Lanka. The question today is not whether enshittification can be stopped, but why regulators in Canada, the US, and elsewhere choose not to stop it [Shapiro1999,Sapp2026].

When the Diagnosis Is Wrong

The case studies above share a pattern: industry denial, manufactured uncertainty, and eventual regulation. Another kind of regulatory failure is equally instructive: one in which the pressure for regulation is genuine and well-intentioned but aimed at the wrong target.

Moral panics about new media are old. Dime novels corrupted working-class youth in the 1880s. Comic books produced juvenile delinquents in the 1950s, according to psychiatrist Fredric Wertham’s Seduction of the Innocent [Wertham1954]. His testimony brought comic books before a Senate subcommittee in 1954 and led directly to the Comics Code Authority, a self-regulatory body that banned dark content so comprehensively that it effectively gutted the medium for a generation. Rock music followed, then Dungeons and Dragons; in each case the new medium was identified as uniquely dangerous to children, its effects described as direct and irreversible, and the evidence offered was a mixture of anecdote, dubious laboratory studies, and credentialed testimony.

Video games arrived in force in 1993, when a Senate subcommittee held hearings focused on two games: Mortal Kombat, which allowed players to rip an opponent’s spine out as a finishing move, and Night Trap, which featured vampires draining blood from actresses in a B-movie setting. The hearings generated excellent television and led directly to the creation of the Entertainment Software Rating Board, the voluntary age-based rating system still in use today. The legislators’ alarm was understandable, but the content that generates maximum outrage is chosen for its visceral impact, not because it represents what most people actually play. The average video game in 1993, like the average video game today, involved puzzles, sports, or platform navigation.

Then in April 1999, two students at Columbine High School in Colorado killed twelve classmates and a teacher before taking their own lives. Both had played Doom. Senators introduced legislation, retailers pulled games from shelves, and the attorney Jack Thompson spent years filing lawsuits claiming games were murder simulators and that the industry bore direct responsibility for the shootings, winning settlements before being permanently disbarred for misconduct.

Dave Grossman offered a more careful version of the alarm [Grossman1995,Grossman1999]. His claim rested on military training research: fewer than a quarter of soldiers in World War II actually fired their weapons in combat, so the Army redesigned its training using operant conditioning with human-silhouette targets to raise that rate in Korea and Vietnam. Violent video games applied the same conditioning to children without consent or any ethical framework. Unlike the public panic, his argument was internally coherent.

Unfortunately, the scientific literature did not validate it. The core methodological problem is that laboratory measures of aggression bear little relationship to real-world violence. Such measures typically involve things like how loud a noise blast a participant gives an opponent, or how much hot sauce they pour for someone who dislikes spicy food. Studies that find effects measure immediate post-game behavior in artificial settings, and the effect sizes are small. Large longitudinal studies, which are better positioned to detect real-world outcomes, consistently find no meaningful relationship between video game consumption and violent behavior. Meta-analyses that account for publication bias—the tendency of journals to publish positive findings—find that the field systematically overestimated effects because studies finding no relationship were less likely to be published [Ferguson2015,Markey2017].

The simplest check on the strong version of the argument is cross-national. Japan and South Korea are among the highest per-capita consumers of video games in the world, and South Korea built a multi-billion-dollar professional esports industry. Both countries have dramatically lower rates of violent crime than the United States. The Netherlands and other Northern European countries show the same pattern. Gun availability, income inequality, and the specific history of racially organized social violence are better explanations for American rates of violence than video game consumption, but are also much harder for the public and public figures to face.

In 2011 the Supreme Court settled the legal question, if not the empirical one. California had enacted a law restricting the sale of violent video games to minors. The Court struck it down in Brown v. Entertainment Merchants Association, applying the same First Amendment analysis it would apply to books or films. The majority noted that the research California presented did not establish a causal link between violent games and harm to minors, and that the burden of proof for restricting expression falls on those who seek restriction. That burden had not been met.

By then the panic was already fading, displaced by fresh anxieties about social media and smartphones. What had not faded were the harms the panic had never addressed. While legislators debated spine-ripping finishing moves, the games industry had been building something that warranted far more scrutiny: loot boxes. A loot box is a randomized reward purchased with real money; you pay to receive an item of unknown value, which may be common or rare. Children’s games marketed this mechanic aggressively to young players. Several European regulators eventually concluded that loot boxes constitute gambling. Belgium banned them in 2018. The United Kingdom’s Gambling Commission produced guidance treating certain loot box mechanics as gambling products requiring the same protections applied to casinos. The United States, whose legislators had spent years fighting over Mortal Kombat, moved slowly.

The lesson is not that moral panics are always wrong, or that industries accused of harm should be left alone. It is that effective regulation requires an accurate diagnosis of the actual harm, not the harm that generates the most compelling congressional testimony. The video game violence campaign failed because the evidence never supported the hypothesis; the energy expended on it left the real harm—addictive design and gambling mechanics in games marketed to children—largely unaddressed. When a proposed regulation is described as protecting children, the right question is not only whether children need protecting, but from exactly what, by what mechanism, and whether the remedy addresses that mechanism.

We don’t have to wait for disaster to start the process. The world dealt with the hole in the ozone layer before it cost lives, and we could choose to act now on social media and AI.

What We Owe the Future

2026-06-15

In 1987, the Welsh government planted 700,000 trees near Newtown to absorb carbon dioxide. Most of those trees won’t reach maturity for another thirty years. The people who planted them knew perfectly well they would never see the benefit. Nobody forced them to do it. They just thought it was the right thing to do.

Defending that intuition is harder than it looks. We normally ground moral obligations in relationships: you owe something to someone because you made a promise, caused them harm, or stand in some ongoing connection to them. Future people don’t satisfy any of these conditions. They can’t help us, sue us, or cast votes. And yet we talk and (sometimes) act as if we owe them something. The question is whether that behavior reflects a coherent moral position or is just a vague sentiment we invoke when it’s convenient and ignore when it’s expensive.

The Case For

Three philosophical traditions give different answers, and they mostly agree. Utilitarians count welfare: if future people will exist, their suffering and flourishing count in the moral calculation. Since there could be many more future people than present ones, their aggregate welfare could easily outweigh ours. This is the premise behind William MacAskill’s argument that the most important thing we can do is improve the long-run trajectory of civilization rather than address present problems [Macaskill2023].

Rights theorists start from a different place but reach a similar conclusion. If future people will have the same moral status as present people—which seems hard to deny—then the fact that they don’t exist yet is morally arbitrary in the same way that geographic distance is morally arbitrary. We don’t think we owe less to someone suffering in a country we’ve never visited. Why would temporal distance be different?

John Rawls asked his readers to imagine designing a society without knowing what position they would occupy in it. He extended this thought experiment across generations: if you didn’t know which generation you’d be born into, you would want rules that protected each generation from being stripped of resources by its predecessors [Rawls1999]. The philosopher Edmund Burke made a version of this point two centuries ago, describing society as a partnership between the dead, the living, and those yet to be born [Burke1790].

These arguments are not identical, but they converge on the idea that our obligations extend beyond the people we can see and hear.

The Problem of Non-Identity

The philosopher Derek Parfit spent years trying to figure out whether any of this actually made sense. His conclusion was unsettling [Parfit1987]. The problem is that the specific people who will exist in the future depend on the specific choices we make today. If we had enacted serious climate legislation in 1990, the people born in 2050 would be different people from the ones who will actually be born. So when we say we are harming future people by burning fossil fuels, which people are we talking about? The people who will actually exist owe their existence to the chain of events that includes our decisions.

This doesn’t mean the future doesn’t matter. It means that if we want to defend obligations to future generations, we probably can’t do it by pointing to individual harm. We need an argument that works at the level of conditions and possibilities rather than identifiable victims.

The Legal Gap

The law has mostly not solved this problem, which explains why our legal obligations to future people are so weak. Courts require an identifiable party who has suffered a concrete injury. Future generations fail this test on both counts.

The workarounds are real but imperfect. Environmental impact assessments require governments to consider long-term consequences. Some constitutions are designed to be difficult to amend, protecting future generations from present majorities. In 1993, the Philippine Supreme Court allowed a group of children to sue the government on behalf of future generations in Oposa v. Factoran, a ruling that has influenced environmental law in other countries. Wales and Hungary have created parliamentary commissioners specifically tasked with representing future interests. These are genuine innovations.

But the structural problem remains: today’s legislators have no electoral incentive to sacrifice for people who cannot vote.

The Case Against, and the Cynicism Problem

The philosophical arguments for intergenerational obligation are more coherent than the arguments against. The non-identity problem is the most serious challenge, and it is a challenge to one particular way of framing the obligation, not to the obligation itself. The utilitarian, rights-based, and contractarian cases all survive it largely intact. What is harder to survive is the behavioral evidence.

The gap between what we say about future generations and what we actually do is not subtle. Greenhouse gas emissions are still rising; fisheries are still being depleted beyond recovery, and pension systems are underfunded while present spending continues. Hans Jonas argued that our technological power has outrun our ethical frameworks precisely because those frameworks were designed for a world where our actions had local, reversible consequences [Jonas1984]. That may be true, but it doesn’t explain why the frameworks have remained so convenient.

The uncomfortable hypothesis is that “what we owe the future” functions primarily as a way of gesturing at seriousness without having to do anything. Thirty years of climate pledges backed by thirty years of continued extraction is fairly strong evidence that the stated preference is not the actual preference. Every cost-benefit analysis used in actual policy applies a discount rate to future welfare, which means a dollar of harm to someone in 2100 is worth almost nothing in present calculations. This is not a fringe view; it is embedded in standard regulatory practice.

There is also a pointed objection from the present: roughly 700 million people alive right now lack clean water, food security, or basic medical care. Diverting moral energy toward speculative future benefits while ignoring identifiable present suffering is a luxury. The language of obligations to future generations can even function as cover for deferring justice for communities already bearing the costs of extraction and pollution, who are disproportionately poor.

This is not an argument that the obligation doesn’t exist. People regularly fail to act on obligations they acknowledge; we don’t conclude that theft is acceptable because people steal. But it is an argument that philosophical clarity about what we owe is doing almost no work in the world, and that the useful question may not be “do we have a duty to future generations?” but rather “what would actually change our behavior?”

When the Future Is Used Against the Present

MacAskill’s longtermism is not merely an academic position. It is the philosophical foundation of a cluster of movements and organizations that have come to exert significant influence over technology policy and philanthropic giving. Émile Torres, a philosopher who helped develop these ideas before becoming one of their sharpest critics, named the cluster TESCREAL: Transhumanism, Extropianism, Singularitarianism, Cosmism, Rationalism, Effective Altruism, and Longtermism. The ideologies in this cluster share the longtermist premise that the scale of the future is so vast that almost any present sacrifice can be justified if it plausibly improves long-run outcomes.

The utilitarian arithmetic is straightforward. If trillions of people could live across star systems over millions of years, then even heavily discounted future welfare vastly exceeds present welfare. A donation that slightly reduces the probability of human extinction is, on this accounting, worth far more than a donation that cures a child of malaria. The future people win the calculation. They always win, because there are so many more of them.

The practical conclusions drawn from this reasoning were not to give to the most urgent present needs but to fund AI safety research, pandemic preparedness for civilization-ending pathogens, and other efforts aimed at preventing extinction or steering the long-run trajectory of civilization. This is not irrational given the premise. The problem is that the premise— that we can make meaningful probability estimates about civilization-scale outcomes centuries or millennia into the future— is not grounded in anything. It is speculation wearing the mathematical clothing of rigor.

The deeper problem is structural. If the future is so important that it swamps present obligations, then whoever controls the trajectory of the future has more moral authority than any present institution or democratic process. This is exactly the reasoning several tech billionaires have adopted. Elon Musk has described his accumulation of capital, media platforms, and satellite networks as necessary to prevent humanity from being locked into a bad long-run future. OpenAI’s founding mission statement—to develop AI safely “for the benefit of all humanity”— is a longtermist framing that positions the organization’s leadership as trustees of civilization accountable to no present constituency. The argument that today’s people must accept disruption, displacement, and concentrated power because future people’s welfare is at stake is being made by very wealthy people about costs borne by others who have no voice in the decision [Andreessen2023].

This is the same rhetorical structure as other forms of extraction: the resources and labor of present people converted into benefits claimed to accrue to a future whose composition and preferences cannot be known, managed by self-appointed trustees who cannot be held accountable because their beneficiaries do not yet exist. The people who will supposedly benefit have no way to confirm or contest the claim. The people bearing the costs can be told that their objections fail to grasp the scale of what is at stake.

The philosophical validity of longtermism as a position about what we owe the future is entirely separable from its political use as a reason why present inequality, present suffering, and present concentrations of power should not be interrupted. An argument that future generations’ interests justify present sacrifice is most credible when the people making the argument are also bearing the sacrifice. It is least credible when it is made by the already-powerful to justify policies that harm people who were not consulted and will not share in whatever future is being optimized for.

Isaiah Berlin spent much of his career analyzing how this kind of reasoning goes wrong at the level of philosophy rather than just politics. His book The Crooked Timber of Humanity took its title from Kant’s observation that “out of the crooked timber of humanity, no straight thing was ever made” [Berlin1991]. In it, Berlin argued that the goods people pursue are genuinely plural and genuinely irreconcilable. Liberty and equality pull against each other. Security and freedom pull against each other. Community and individuality pull against each other. These are not failures of imagination or coordination problems awaiting a technical fix. They are permanent features of human life, and no political arrangement will eliminate them.

The view Berlin was arguing against he called monism: the belief that freedom, utility, the glory of the nation, the arc of history, expected future welfare, or something else is the one supreme value, and that apparent conflicts between goods are ultimately solvable by reference to it. Monism is not just an abstract error. It is, Berlin argued, the philosophical root of political tyranny, not because monists are necessarily cruel but because once you accept that one value is supreme, you can justify any present harm. If the supreme value is large enough—and nothing is larger than the welfare of all future humanity—the calculation can authorize anything done to anyone living now. The people bearing the cost can always be told that their objections reflect a failure to grasp the scale of what is at stake.

Liberal democracy, on Berlin’s account, is not a system for finding the right answer. It is a system specifically designed for a world in which there is no right answer—in which the goods people legitimately pursue are inevitably in tension and the best that can be achieved is a negotiated, revisable, accountable balance among them. What TESCREAL bypasses is not merely democratic process as a procedural nicety. It is the entire institutional architecture designed to prevent any single value, and any single group of people claiming to serve that value, from overriding the rest.

The Material Basis of Democratic Accountability

The philosophical argument that longtermism justifies concentrating power has a concrete political parallel that requires no philosophy to understand. Democratic governance rests on a bargain. The governed have things that their governors need: labor, taxes, and consumer spending. This dependency is the material basis of democratic leverage. When value is generated by AI systems owned by a handful of corporations already expert at tax optimization, every mechanism of democratic governance weakens simultaneously. The tax base shrinks, consumer spending contracts, and collective bargaining fades away. The tendency for the return on capital to exceed economic growth accelerates because AI severs the last link between capital accumulation and the need for human labor; as that happens, wealth becomes concentrated in fewer and fewer hands [Piketty2017].

The economic incentives point toward entities with the fewest democratic accountability mechanisms. An authoritarian government that deploys AI to replace its workforce faces no electoral consequences and gains a surveillance and control dividend on top of the economic efficiencies. Governments with vast capital, centralized decision-making, and no electorate to answer to are structurally better customers for this technology than democracies. This is one concrete reason the political movement around AI acceleration has shifted support toward figures who have demonstrated no loyalty to democratic constraints [McGrann2026]. Dario Amodei, the CEO of Anthropic, said it directly: “The balance of power of democracy is premised on the average person having leverage through creating economic value. If that’s not present, I think things become kind of scary.” The CEO of one of the leading AI companies is describing the technology he is building as a threat to the material basis of democratic governance. His company has not endorsed a single piece of legislation to address it.

What We Can Defend

Years ago, while hiking in the woods, I spotted a set of bamboo windchimes hanging from a tree. There were five weather-stained rods carved to different lengths, each of which had initials carved on it and a pair of dates. I can’t remember the years exactly—this was back in the days of chemical cameras, so if I still have the photo I took it’s in a box in my basement—but I remember they ran from the 1930s and 1940s to the 1980s.

The bar the chimes hung from had a hole carved in it for a sixth. I still sometimes wonder why it was missing. Had a squirrel chewed through the twine used to tie it on, so that it fell into the undergrowth and was lost? Had someone’s time not come yet? Or had there been no one left who knew or cared to add it?

After all I’ve read, I believe that caring about the future is a personal choice, and that the most defensible version of our obligation to it is simple: don’t foreclose options. I think we should strive to give our grandchildren, and their grandchildren, as many choices as we had. They should be able to enjoy the things that we can enjoy, like safe drinking water, healthy coral reefs, and free elections. I believe that we don’t own those things. We are just their trustees, and like all trustees, we are accountable for what we hand on—even if the beneficiaries aren’t in the room.

More Analogies

2026-06-14

These posts started as a series titled “Big Tech is Like…”. An earlier post in this series included eight of those; here are a few more.

Big Tech is Like a Company Town

E.P. Thompson’s account of the transition from putting-out to factory production made a point that factories were initially not more efficient at producing cloth: they were more effective at eliminating worker autonomy [Thompson1963]. Before industrialization, workers controlled their own time and pace, could share work across household members, and could resist disadvantageous terms through slow work, variable quality, and informal coordination. The factory closed off these responses: workers arrived at fixed times, worked at a supervised pace, and until labor unions emerged, had no practical means of collective withdrawal. The shift to app-based platform work is doing the same thing today. The app is not merely a more convenient interface. It is supervisory infrastructure that monitors pace and completion rates and eliminates the degrees of freedom that less tightly managed arrangement permitted.

George Pullman built Pullman, Illinois in the 1880s alongside his railroad car factory: a planned community where the company owned the workers’ housing, stores, church, library, and bank. Workers were paid partly in scrip redeemable only at company stores. When Pullman cut wages during the depression of 1893 while holding rents fixed, workers could not cushion the blow by cutting other expenses, because the company controlled those too. They had no recourse and nothing left to lose. The 1894 Pullman Strike paralyzed rail traffic across the country and required federal troops to suppress, which tells you something about what total control eventually produces.

A developer whose business depends entirely on the App Store, a seller whose inventory and customer relationships live on Amazon, or a creator whose audience exists only within a single platform is in an equivalent position: the terms can be changed at any time, the cost of departure is prohibitive, and the entity that provides the housing also adjudicates disputes about it.

What the company town reveals is something market economics tends to obscure: the difference between an employer and an infrastructure provider is a matter of degree, not kind. An employer controls your income; an infrastructure provider controls the conditions under which you can earn, spend, and participate in social life. Pullman did not merely employ his workers: he owned the physical environment in which they lived, which meant that the power relationship extended past the end of the workday into every dimension of daily existence.

Pullman justified all of this in the language of paternalistic improvement [Lindsey1942]. The company town was presented as a planned community designed to give workers superior housing, sanitation, and amenities compared to what they could obtain elsewhere. This was partly true: Pullman, Illinois was better built than most industrial housing of the period. But the justification also framed the absence of collective bargaining as a feature rather than a constraint. Workers who were being improved did not need unions. They needed to trust the judgment of the person improving them. Conveniently, this gave the appearance of converting a power arrangement into a benevolent one.

The Pullman case was not unusual. Coal patches in Appalachia and the Canadian Maritimes operated on the same model: company-owned housing, company stores where scrip could be spent, company control of access roads and community infrastructure. In the post-Civil War South, plantation stores extended dependency relationships across the formal end of slavery, tying sharecroppers to specific land through debt that was nearly impossible to escape without leaving behind the credit, relationships, and community standing that they depended on. Mining camps across Latin America reproduced the same structure under different flags. The company town is a recurring organizational form because it solves a genuine problem for whoever controls it: it makes exit nearly impossible while appearing to be voluntary.

The distinction between short-term dependency and structural lock-in is what keeps workers and developers in arrangements that are getting worse. Short-term dependency is a rational calculation: you stay because the current terms are good enough and leaving has costs. Structural lock-in is different. The costs of leaving have been deliberately raised so that the calculation does not change even as the terms worsen. A platform can worsen its terms incrementally; each incremental worsening is individually insufficient to justify the exit cost, even if the cumulative total is large.

A more extreme form of this appears in the danwei, the work unit system through which Mao-era China organized urban life. The danwei was the unit through which citizens accessed employment, housing, healthcare, food rations, and education for their children. To be removed from a danwei was to lose access to all of these simultaneously. The contemporary parallel is super-apps like WeChat that integrate messaging, payments, healthcare bookings, government service access, merchant discovery, and social identity in a single system. A user whose account is suspended loses not just a communication tool but the infrastructure through which they conduct their civic and commercial life. The Pullman Company controlled the economic conditions of workers’ lives. The super-app, at its most developed, aims to be a danwei [Stoller2019,Walder2017].

Big Tech is Like the Beauty Industry

The beauty industry does not merely sell products. It works by making people believe they have flaws they were not previously considered flaws, by creating status competition around those flaws, by ensuring that the standards people are supposed to meet remain unstable, and then sells temporary relief from the insecurity it has helped create. The customer is never meant to arrive, because that would end the business model.

The same logic governs much of the digital economy. Platforms do not simply sell access to information, communication, or entertainment. They produce social comparison at industrial scale, amplify the feeling of falling behind or being left out, and then sell ads, subscriptions, and self-optimization tools as the remedy.

Thorstein Veblen identified all of this, even though he was writing in 1899 rather than in the age of Instagram. The point of status goods is not that they are useful or pleasurable. It is that they are visible evidence of social rank. The phrase “keeping up with the Joneses”, which entered American English in the early twentieth century, names the same dynamic more plainly. Consumption becomes competitive because social standing is comparative, which means there is never “enough”. This competition is profitable because the goalposts move as soon as people approach them [Veblen1899].

The beauty industry is a particularly efficient machine for converting status anxiety into revenue because it treats the human body as a perpetually unfinished project. Skin can always be smoother, hair shinier, teeth whiter, age less visible, and weight lower. What matters is not whether any particular intervention works as advertised. What matters is the manufactured belief that our peers are taking action, so we must too. This is why beauty advertising oscillates between aspiration and warning: you want to be admired, but you fear that you will also be judged.

Women have always borne the brunt of this manufactured insecurity. The modern beauty industry developed alongside labor markets and marriage markets in which women’s economic security and social standing were tied more closely to appearance than men’s. As legal barriers to women’s advancement weakened, appearance standards became a more intense disciplinary mechanism, not a less important one. A market built around telling women that their bodies require continuous corrective spending fits very comfortably inside a misogynist social order [Peiss2011].

Big tech has given this machinery telemetry, automation, and scale. The platform feed is a Jones machine. It puts other people’s vacations, kitchens, bodies, weddings, and workout routines in front of us not as occasional local gossip but as a continuous global stream. The result is not just envy: it is the normalization of comparison as a default mental state. Am I fit enough, rich enough, busy enough, stylish enough, politically informed enough, raising my children well enough, or aging well enough? A system that can keep those questions humming in the background can monetize them indefinitely.

The comparison does not have to be truthful to be effective. Beauty advertising has always relied on lighting, retouching, and selective casting. Social platforms inherit all of that and add filters, algorithmic selection, and engagement optimization. The most attention-grabbing images are not the most representative ones. They are the ones most likely to intensify feeling s admiration, desire, resentment, or shame. The platform does not need users to believe that everyone else is happier, younger, or richer than us. It only needs users to feel, for a few seconds at a time and many times a day, that they are falling behind.

Influencer culture is itself manufactured. The beauty industry has long depended on the blurring of intimacy and commerce. Advice from a beautician and endorsements by actresses all worked because they borrowed the authority of friendship or expertise while remaining commercial speech. Influencers do the same thing with better metrics. They present consumption as personality and sponsorship as authenticity.

This is also where misogyny returns in a more explicit form. Women are subjected to the harshest forms of visual ranking online, and platforms profit from that ranking regardless of what they say in public about empowerment or self-expression. Image-heavy systems reliably reward content that conforms to existing beauty norms while presenting itself as spontaneous self-presentation. The labor required to produce that appearance is substantial and usually hidden. The result is an economy in which women are pushed to become both product and salesperson, while the platform captures a share of every transaction.

The beauty industry also helps explain why the language of choice is inadequate. No one is forced to buy a serum or post a filtered selfie, but choices made in response to organized social pressure are not free in any meaningful social sense. A teenager deciding whether to participate in appearance-based competition on a platform is making an individual decision inside a system designed to make non-participation costly, just like a programmer deciding to maintain a LinkedIn presence full of visible hustle and polished enthusiasm. The compulsion is social before it is economic, and economic because it is social.

What the beauty analogy adds to our understanding of big tech is not just that platforms sell ads—every industry does that. It is that platforms are in the business of manufacturing dissatisfaction and organizing it into recurring revenue. Their entire business model is that you are not quite enough yet, but perhaps one more purchase will fix that. A business model built on that premise has no interest in ever letting us feel finished [Packard2007,Wu2016].

Big Tech is Like the Sharecropping System

After the American Civil War, former slaves and poor white farmers in the South farmed land they did not own under a system called sharecropping. Contracts that gave the landowner a percentage of the harvest, required them to buy supplies on credit from the landowner’s store at prices the landowner set, and prohibited them from selling to anyone other than the landowner. The debt was structured so that a bad harvest (or even a good one, depending on how the accounts were kept) left the farmer owing more at year’s end than at the beginning. The system was legal, entered into “voluntarily” (by people with no other options), and reproduced the economic relations of slavery without the formal institution.

Historians and economists have found systematic underweighting of harvests and overcharging of credit accounts. The system was designed to perpetuate indebtedness, not resolve it. To make a bad situation worse, the merchant providing supplies was often the same person as the landowner; if not, they usually operated as a tied supplier. The farmer could not buy supplies from a competing merchant because the crop lien pledged the entire harvest to the furnishing merchant as collateral for the advance. “On credit” prices at the furnishing merchant’s store were set at a markup over cash prices that could run from thirty to sixty percent.

By pledging next year’s crop to cover this year’s debt, the farmer legally committed future labor before that labor was performed. This is the structure of platform lock-in: time and effort are committed to a platform before the platform’s terms are known for the following year. A sharecropper who improved the land was more trapped, not less, because the accumulated investment had nowhere else to go. The same is true of a creator who has built a hundred thousand subscribers on a platform that then changes its revenue-sharing terms.

Peonage was the name for debt arrangements that crossed from exploitative into criminal. It refers to holding a worker in involuntary servitude through debt while using threats or actual violence to prevent them from leaving. Federal peonage statutes were enacted after Reconstruction, and some prosecutions did occur in the early twentieth century after investigative journalism and advocacy exposed conditions in the turpentine camps and cotton plantations of the Deep South. These prosecutions targeted individual employers; they did not address the systemic accounting fraud or the market structure that produced mass indebtedness [Daniel1972,Blackmon2008].

The platform equivalent of this operates through percentage fees, arbitrary algorithmic changes, and paid promotion requirements. When YouTube changed its monetization criteria in 2018 to require one thousand subscribers and four thousand watch hours before a channel could earn advertising revenue, channels that had not yet crossed those thresholds were cut off from income they had been building toward. When organic reach on Facebook declined sharply for business pages after 2012, businesses that had invested in building Facebook audiences were told they could pay for promotion to reach the audiences they had already acquired. The audience is the crop; the algorithm change is the landlord raising the rent after the harvest is in.

A related but distinct mechanism appears in gig economy work, which parallels the putting-out system of early modern textile production. Merchants in eighteenth and nineteenth century Britain supplied raw materials to rural householders, who processed them at home using their own equipment. The merchants then collected the finished goods at prices they set. The household owned its tools; the merchant owned the raw material, the finished product, and the customer relationship. If demand fell, the merchant stopped delivering material and the household had to absorb the income shock. The merchant’s flexibility was the household’s precarity, by design [Devries2008].

Gig economy platforms reproduce this structure. The delivery worker owns the car or bicycle. The platform owns the customer relationship, the pricing mechanism, and access to the market. A worker who attempts to find customers outside the platform risks deactivation, which functions as accusations of theft or embezzlement did in the putting-out system: because formal ownership of the customer relationship belongs to the platform, any attempt to access that relationship independently constitutes a violation of the terms of service that governs continued access to work.

Big Tech is Like a Fast Food Franchise

A McDonald’s franchisee invests several hundred thousand dollars building and equipping a restaurant, recruits and trains staff, manages daily operations, and absorbs the risk of a bad location. McDonald’s sets the menu, the supplier list, the pricing guidelines, the design specifications, the training requirements, and the standards against which the franchise can be audited and revoked. The franchisee’s capital is at risk; McDonald’s corporate’s is not. McDonald’s also, in most arrangements, owns the real estate and charges the franchisee rent. The arrangement is voluntary in the sense that no one forces the franchisee to sign. It is asymmetric in the sense that every significant decision rests with one party.

The franchisee agreed to all of this in advance.

McDonald’s corporate revenue derives primarily from real estate, not from food. The company acquires the land and building, then leases them to the franchisee at rates that capture a substantial share of the location’s economic value regardless of operating performance. Franchise royalties are calculated as a percentage of gross sales, not profit, meaning the franchisor collects whether the franchisee makes money or not. The hamburgers are the mechanism by which the real estate and royalty revenue are generated. This structure is not incidental to the model; it is the model.

The parallel to third-party sellers on Amazon, app developers on iOS and Android, and content creators on social media is structural: the platform sets the rules, extracts a percentage, and can change the terms or terminate the relationship on short notice [Schlosser2001].

Platform fees work like franchise royalties: they are a take on gross transaction value, not on seller profit.

Apple’s standard rate is thirty percent of revenue from in-app purchases, a figure that it set unilaterally and has adjusted modestly only under regulatory pressure. The developer has no alternative distribution channel for iOS users, because Apple prohibits sideloading and third-party app stores on its platform. This makes the fee unavoidable for any developer who wants access to iOS customers. What’s more, Apple and Google review apps before they are listed, can remove them after listing, and adjudicate appeals internally. There is no neutral third party with authority to override a platform’s decision.

All this creates an investment trap for platform participants. A seller who has accumulated reviews, rankings, and sales history on Amazon cannot transfer that reputation to another marketplace; a developer whose app has a rating history on the App Store cannot move that history to Google Play. This is not an accident: it is the mechanism that keeps participants in the system after the platform has extracted the value of introducing customers to them.

Antitrust law has historically been reluctant to treat voluntarily agreed contractual terms as coercive, even when the practical alternatives to agreement are limited. The prevailing view is that if the franchisee had an alternative and chose this arrangement, the arrangement is presumptively legitimate. This framing does not account for information asymmetry at the time of contracting or the way switching costs increase over time. And it does not account for the fact that the “voluntary” choice is often a choice between accepting one set of platform terms or not accessing a market at all [Stoller2019].

Big Tech is Like Scientology

In the 1950s, L. Ron Hubbard developed a practice called auditing. In a standard session, a trained Scientology auditor asks the subject a series of questions while the subject holds the electrodes of an E-meter that measures galvanic skin response (the same physiological signal used in polygraph tests). The questions are designed to surface traumatic memories, which Scientology calls engrams, so they can be discharged through conscious recall.

The sessions are recorded, and the records are kept in what Scientology calls “preclear folders.” They contain whatever the subject disclosed during auditing: accounts of illegal activity, sexual behavior, family conflicts, financial difficulties, and statements about other people. The Church of Scientology denies that folders are used punitively, but former members have testified that these folders were used in disciplinary proceedings and in litigation against critics and defectors.

The analogy to big tech is not subtle. Every major social media platform is, at its core, an auditing system. It collects behavioral data—what you look at, what you hesitate over, what you react to—and that information is qualitatively different from what you share with a retailer. People post about illness and grief and their political beliefs and sexual identity because the platform presents itself as a community, not a database. The fact that it is both doesn’t mean the user is naïve; it means the platform is designed to exploit the social context that makes sharing this information feel appropriate.

Scientology’s critics have documented a practice the Church calls Fair Game, under which people who leave the organization and speak critically about it (known as a suppressive person) can be “deprived of property or injured by any means by any Scientologist without any discipline of the Scientologist.” The Church claims this policy was cancelled in 1968, but its critics have documented its continuation under different names. The pattern has included litigation designed to exhaust defendants financially, harassment campaigns targeting employers and family members, and the use of auditing records in legal proceedings.

Tech companies have not employed anything comparable in severity (that we know of). They have, however, used legal and institutional power to manage criticism in ways that Scientologist would recognize. Facebook commissioned audits of third-party researchers who published findings the company disputed. Google funded academic research in ways that created conflicts of interest for academics who might otherwise study the company critically. Uber deployed a team it internally called COIN (for Competitive Intelligence) to gather information on regulators, journalists, and competitors. The distinction between these practices and Fair Game seem pretty slim to the researchers, journalists, and regulators on the receiving end.

Scientology’s governing doctrine holds that the organization’s critics are necessarily criminals. If someone attacks Scientology, Hubbard’s writings state, one need only look at their past to find the crimes they are hiding. The logic is airtight because it is circular: criticism itself is taken as evidence of wrongdoing.

This is a specific and pathological version of a general tendency. When researchers publish findings critical of Facebook’s recommendation algorithms, Facebook’s communications team responds not only with factual rebuttals but with questions about the researchers’ methodology, funding sources, and motivations. When journalists publish stories based on leaked documents, companies issue statements about documents being “taken out of context” and about reporters’ prior relationships with the company.

Scientology is structured so that participation becomes progressively more expensive. New members begin with free or low-cost introductory materials. Progression up the “Bridge to Total Freedom” requires increasingly expensive courses and auditing sessions. Former members have documented spending hundreds of thousands of dollars over years of participation. The social world of Scientology reinforces continued involvement: friends, family, and community ties are largely internal to the organization, which means that leaving means losing them.

The structural lock-in that platforms engineer follows the same logic. A photographer who has spent years building an audience on Instagram is not free to leave without abandoning what they have built. A developer who has built a business on the iOS App Store faces the same kind of switching cost.

The Church of Scientology has survived decades of hostile press, regulatory action across multiple continents, and prominent defections. It has done so by treating litigation as a cost of doing business, and by providing genuine community to members. The question now is whether the mechanisms that have gradually constrained Scientology will operate at the scale of companies whose products are used by billions of people who have no obvious alternative [Wright2013].

Big Tech is Like a Ransom Business

The Canvas learning management system was hacked a couple of days ago, so this seems like a good time to point out that extortion, if it’s professional enough, is indistinguishable from any other fee-for-service arrangement. The victim pays for the return of something that was theirs, the captor provides a guarantee of safety, intermediaries take a cut, and everyone has an interest in the transaction completing cleanly.

In 1994, when the FARC guerrilla organization in Colombia was near the height of its power, kidnapping was a line item in its budget. The organization maintained specialized units for identifying targets, executing abductions, holding captives in jungle camps, and conducting negotiations. Insurance companies led by Lloyd’s of London responded by creating kidnap-and-ransom (K&R) policies for multinational corporations, and specialist firms like Control Risks Group built a business on negotiating with kidnappers. By the late 1990s, an abduction in Colombia, Venezuela, or the Philippines was like buying a house: the kidnapper demanded a high figure, the negotiator offered a low one, and after weeks or months of back-and-forth they agreed on something in the middle and settled up in cash.

Both sides had an interest making this running smoothly; in particular, kidnappers who killed hostages damaged their own reputations with future potential clients. Researchers studying the “industry” found that K&R specialists worked hard to prevent ransom inflation: they trained negotiators to push back, kept payment records confidential, and advised clients not to advertise their coverage, because a public policy was an advertisement for kidnapping your staff [Shortland2019].

The rise of ransomware attacks over the last decade has followed the same path. The 2017 WannaCry attack encrypted hundreds of thousands of computers across 150 countries in a single weekend, demanding Bitcoin payments in exchange for decryption keys; the attack was later blamed on North Korean state actors. Four years later, the DarkSide ransomware group (probably based in Russia) shut down the Colonial Pipeline in the United States and demanded approximately $4.4 million in Bitcoin. The company paid within hours.

Modern ransomware groups operate on an affiliate model: the core developers write the malware and maintain the payment infrastructure, while affiliates handle the actual intrusions. On the other side of the table, cybersecurity firms handle the details just like Control Risks Group did, and cyber insurance policies now cover ransom payments, which means that insurance companies are wrestling with the same concerns about moral hazard and ransom inflation that Lloyd’s was worrying about in the 1990s.

When Colonial Pipeline paid DarkSide, they almost certainly broke US Treasury rules prohibiting payments to sanctioned entities. Governments have been consistently inconsistent in their positions on this: they urge companies not to pay while acknowledging privately that there is no realistic alternative. This is the same ambivalence that surrounded K&R payments in the 1980s, when Western governments officially discouraged negotiating with kidnappers while intelligence services routinely assisted with exactly that [Dudley2022].