When the Model Is the Harm

My original idea for these posts was to write a series titled “Big Tech Is Like…” The sections below were fun to write; I hope they’re also fun to read.

See the first post in this series for context.

Big Tech is Like a Drug Cartel

[Wainwright2017] is one of my favorite books. In order to show how the free market really works, he went and studied it in its pure, unconstrained form: the cocaine cartels. It was a fun and insightful read, and ever since I first encountered it I’ve wanted him to go back and do a similar book about big tech companies. After all, they too sell artificially addictive products, treat the legal system as a mere expense, and are run by sociopathic narcissists.

Wainwright’s thesis is that every business faces a common set of problems: how to maintain product quality, how to protect market territory, how to enforce agreements with suppliers, how to recruit and manage staff, and how to handle disputes with competitors. Legal businesses deal with these issues through a combination of contract law, trademark and patent protection, employment law, and (if all else fails) litigation.

Drug cartels solve the same problems by different means, and their solutions reveal some interesting things about how business actually works. Take brand protection, for example. A consumer who buys a product with a known brand name has some assurance of consistent quality because the brand owner has reputational incentives to maintain standards and can take legal action against counterfeit goods.

A cocaine cartel can’t use trademark law. It can, however, use violence against competitors who sell adulterated product under the same name or who operate in territory the cartel has claimed. The aim—maintaining exclusivity and quality signals—is the same, it’s just the mechanism that differs. (And yes, the word “just” is doing a lot of work in that sentence.) However, violence is like litigation: it’s expensive, it’s noisy, and you might lose in the end, so you have a lot of reasons to try to negotiate a settlement, even if it’s a bad one.

Worker relations are also similar. Employment law has evolved over the last hundred and fifty years to reduce mistreatment of workers through minimum wages, safety requirements, and prohibitions on arbitrary dismissal. People who work in the drug trade have no such protections. However, the brutal labor relations practices of the cartels are not a consequence of the personalities involved. They are instead the result of removing the legal protections that exist precisely to prevent brutality and exploitation in legal labor markets.

As a final example, consider vertical integration. A business that controls its entire supply chain is less vulnerable to supplier failure, price gouging, and quality problems. Legal businesses achieve this through acquisition and contract, but face antitrust limits on how far they can consolidate. (At least, they used to: antitrust enforcement has been steadily weakened since the early 1980s.)

Cartels can’t sue suppliers who fail to deliver, so they have a powerful incentive to own suppliers outright. The cartels that have achieved the most durable market positions have typically integrated vertically as far as they can, unconstrained by antitrust law.

Tech companies use the term “disruption” to describe entering and reorganizing an existing market. From the perspective of the incumbent businesses in that market, disruption means that the pricing norms, labor deals, and the competitive equilibria they had established are attacked by a new entrant willing to operate at a loss or outside the regulatory frameworks that the incumbents are bound by. Drug cartels are, in this sense, serial disruptors: they subvert the legal system to undercut incumbents when they enter markets, often at great cost to local communities.

If this sounds like the way Uber, AirBnB, and others have operated, that’s not an accident: the connection between market concentration and the capacity to externalize harm is a structural feature of markets, not something specific to drug cartels. A highly concentrated firm in any industry has reduced competitive pressure, which means it can absorb the reputational cost of poor labor or environmental practices without losing market share. It can also use its political leverage to shift the cost of its harms onto workers through suppressed wages, onto communities through pollution and infrastructure strain, and onto governments through underfunded services and tax avoidance. Cartels exhibit this pattern in its most extreme form because they face the weakest institutional constraints, but the underlying logic is no different from what Google, Meta, and others have been doing for years.

The safeguards of legal markets that people take for granted in first-world democracies were invented specifically to prevent the damage done by unregulated markets. Minimum wage laws, environmental standards, workplace safety rules, antitrust regulations, and consumer protection statutes are society’s equivalent of immune reactions. Each was lobbied against by the industries they constrained on the grounds that regulation would be impossible, ineffective, immoral, expensive, or harmful to national security. As we belatedly start to think seriously about regulating social media and AI, I think it’s worth keeping the cocaine cartels in mind [Skarbek2014].

Big Tech is Like a Long Firm Fraud

Another book that I really enjoyed was [Davies2022], which is about how legendary frauds reveal the workings of the world. while he doesn’t discuss big tech, the parallels are inescapable. Understanding the classic patterns of fraud laid out in Davies’ book makes it possible to identify them in the software industry, which is not yet regulated well enough to make them prosecutable.

Fraud is more common than prosecutions suggest, and its most dangerous forms are not easily recognized as fraud even by the people committing them. Many frauds begin as genuine optimism, turn into rationalization, and end in deliberate concealment. By the time the fraud is clear from the outside, its perpetrators may have so thoroughly internalized their own narrative that they are genuinely shocked by the prosecution. There is often no clear moment when they knew knew they were crossing a line.

The semi-technical definition of fraud is “a deliberate misrepresentation of material fact that causes someone to act to their detriment and the deceiver’s benefit”. It is easily confused with incompetence, negligence, and bad luck, but it is none of those things. A startup that believed its technology would work and turned out to be wrong is a business failure. A startup that knew its technology did not work and told investors and customers that it did is fraud. The difference is difficult to establish because (a) people can deceive themselves or misremember events and (b) they can also just lie.

Three kinds of fraud are particularly relevant to tech companies. A Ponzi scheme appears to generate returns by paying early investors with money from later investors, rather than from genuine investment gains. Each successful payment increases the scheme’s credibility and attracts additional investors. The schemer does not need to maintain a lie in the face of contradictory evidence: the evidence available to most participants is the evidence of payment, which is real. However, the scheme can only continue as long as new investment exceeds required payments. When new investment slows and required payments exceed available funds, the scheme collapses. The timing of the collapse is usually determined by market conditions outside the schemer’s control. This structure means that a Ponzi scheme operator may not know when the collapse will come, which provides an incentive for them to fool themselves as well as their investors. After all, the scheme worked yesterday—maybe it will work again tomorrow.

A long firm fraud operates on a longer timescale. The fraudster starts by building a legitimate-seeming business, establishing a track record of reliable transactions and prompt payments. Once they have a reputation for reliability, they use that to place large orders on credit, receive the goods, sell them quickly at a discount for cash, and disappears before the creditors can collect. The victim’s mistake is not in trusting the operator—the early trust is actually warranted. The mistake is failing to notice that the scale and urgency of later transactions is inconsistent with normal business patterns.

Control fraud operates inside a legitimate company rather than through a fictitious one. An executive who controls a company can use that control to route contracts to related parties, manipulate reported earnings to maximize personal compensation, extract value through structured transactions that are difficult for outside observers to interpret, and maintain an appearance of legitimacy for years. William Black’s The Best Way to Rob a Bank Is to Own One described the role control fraud played in the savings and loan crisis of the 1980s, and how it was made possible by Reagan-era deregulation that removed oversight mechanisms, and by accounting standards that gave executives wide latitude in reporting. The auditors who certified the books were paid by the companies they audited and had professional and commercial incentives to take management’s claims at face value [Black2005].

Finally, accounting fraud works because accounting requires judgment at every stage where fraud can be inserted. Revenue can be recognized early or late; assets can be valued on optimistic or conservative assumptions, and liabilities can be disclosed prominently or buried in footnotes. Each of these choices meets professional standards and is individually defensible. Where the fraud comes in is consistently choosing the most aggressive position in every ambiguous case. Auditors frequently fail to catch accounting fraud because of the volume of material they need to review, and because their adversaries anticipate the specific tests that will be used to detect it. Most importantly, auditing fails because big accounting firms depend on their clients for their income, which gives them a powerful reason to not find any problems [Palazzo2025,Perrow1999,Schneier2023].

Big Tech is Like the Enclosure Movement

Email, RSS, the open hyperlink, and the early web were commons: shared infrastructure anyone could build on. Social platforms converted that commons into walled gardens, moving the audience inside and charging rent for access to it. In doing this, big tech companies were following a centuries-old playbook [Bollier2014].

The idea that an individual can own a piece of the earth’s surface is younger than most people realize, specific to certain legal traditions, and was imposed on much of the world by force. In most places throughout most of history, land was managed collectively via overlapping use rights rather than exclusive ownership. Understanding how private property in land was created, and what was destroyed in the process, lets us ask clearer questions about property rights generally: not “is this property?” but “who decided it was property, when, why, and who was dispossessed in the process?”

The best-known example of communal land being privatized (to English speakers, anyway) is the enclosure movement, which peaked in the second half of the 18th century. Before enclosure, common land was governed by overlapping, community-enforced use rights: the right to graze a specific number of animals, to cut timber for fuel, or to fish particular stretches of a river. Elinor Ostrom’s research on commons governance documented how these systems were managed sustainably for centuries without either private ownership or state control through locally developed rules, monitoring, and graduated sanctions [Ostrom2015]. The commons worked; the argument that they were inherently prone to overuse was made by enclosure’s beneficiaries and repeated long after it had been empirically refuted.

Parliament passed hundreds of private Enclosure Acts between 1750 and 1850, each one extinguishing common rights over a specific area and converting that land into private property. The process was not neutral arbitration between competing claims: the landowners who stood to benefit were the same class that controlled Parliament. The commoners whose rights were extinguished had no equivalent political representation; when they received any compensation at all, it was consistently inadequate. The result was a large-scale transformation of semi-independent smallholders into wage laborers.

The English enclosure model was not confined to England. It was carried outward through colonialism as one of the primary instruments of dispossession. In Ireland, India, sub-Saharan Africa, and the Americas, colonial law systematically refused to recognize collective or customary land tenure. Land that was not individually titled under a system legible to European courts was declared waste, Crown land, or legally available for settlement. This did to colonized peoples what the Enclosure Acts had done to English commoners: extinguish practices that had sustained communities for generations, convert resource into commodities that could be extracted or sold, and make the people dependent on their new overlords [Linebaugh2014].

Big Tech is Like Professional Wrestling

The French philosopher Roland Barthes wrote about wrestling in 1957. He wasn’t writing about sport; he was analyzing a form of entertainment that reveals something important about how spectacle works, and his argument was that professional wrestling is pleasurable not despite being scripted but because it is scripted. The audience is not there to watch a fair athletic contest. It is there to watch the theatrical enactment of moral archetypes: the villain who cheats openly, the hero who suffers before triumphing, and the referee who is briefly fooled before justice prevails.

The industry term for this collective fiction is kayfabe. Wrestlers maintain kayfabe in public appearances, in interviews, and on social media, even though every adult in the audience knows that wrestling is a performance. This isn’t deception: it’s a mutual agreement to treat the fiction as real to make the experience more emotionally satisfying. When a wrestler breaks kayfabe—acknowledges the performance from inside it—it violates a social contract, not a factual claim [Barthes1972].

Big tech runs on a remarkably similar agreement. Consider the long rivalry between Apple and Google. Apple has spent decades positioning itself as the champion of user privacy, in explicit contrast to Google’s surveillance-funded advertising model. Tim Cook has delivered speeches describing privacy as a “fundamental human right.” Apple introduced App Tracking Transparency, which requires apps to ask permission before tracking users across other platforms. This is the hero narrative. Google, cast as the villain, harvests behavioral data at industrial scale.

What the kayfabe obscures is that Apple earns approximately $20 billion a year from a deal that makes Google the default search engine on every iPhone sold. The two rivals are financially interdependent, and each needs the other to play its designated role. Apple can charge premium prices for privacy-branded hardware, and Google gets default distribution to Apple’s affluent user base in exchange for revenue that Apple would otherwise have to replace. Neither company has any incentive for the match to end.

In wrestling, the heel is the designated villain: the character whose job is to be so obviously wrong that the audience rallies behind whoever fights them. Tech companies take turns playing this role. Mark Zuckerberg testifies before the US Senate, unable to answer basic questions about his own platform, and for a season or two Meta is the villain everyone agrees on. Then a new scandal breaks, the spotlight shifts, and a new villain takes the stage.

The rotation is not accidental. Sustained outrage at one company might produce structural reform. Outrage distributed across all of them produces blog posts like this.

The referee, in this reading, is the regulator. Referees in professional wrestling apply the rules as written, but the rules are designed to produce a compelling show rather than a genuinely fair contest. The EU’s record fine against Google of €4.34 billion for Android antitrust violations in 2018 amounted to roughly two weeks of Google’s annual profit at the time. Google appealed, had the fine reduced slightly, and continued the practices it had been fined for.

Barthes concluded that the real function of wrestling is to transform suffering into spectacle, and spectacle into something that feels like justice. The audience leaves satisfied because the villain was punished. It does not matter that the match was scripted, because the emotional experience of moral resolution is genuine.

This is what congressional hearings about social media accomplish for most people who watch them. A senator reads a damning internal email, a tech CEO says something evasive, the clip circulates for a couple of days, and people feel that accountability happened. The executives return to their offices, the company eventually pays a settlement with no admission of wrongdoing, and the market structure that produced the problem continues unchanged.

Kayfabe is not lying. It is a shared agreement to experience something as if it were real, because that is more satisfying than experiencing it as what it actually is. The question to ask about tech’s performance of competition, concern for users, and openness to accountability is not whether individual participants believe what they say. Some of them probably do. The question is whether the performance is producing structural change or whether it is performing structural change for an audience that finds the performance sufficient [Frankfurt2005,Mazer1998].

Big Tech is Like the Penny Press

The business model in which content is provided free to audiences while advertisers pay to reach them did not begin with social media. It predates the web and television alike, beginning with newspapers in the 1830s and refined through a century of radio and broadcast television. The technology changed but the logic did not: the audience is not the customer but the product, and the content is the mechanism for assembling and sorting that audience.

The penny press emerged in New York in the 1830s when publishers like Benjamin Day realized that a newspaper priced at one cent could not sustain itself through subscription revenue but could attract large enough readership to sell advertising at rates that more than covered costs. The New York Sun and its imitators were not selling information to readers; they were selling readers to advertisers. Sensational crime reporting, gossip, and human-interest stories were not editorial lapses: they built the audience that made the advertising worth buying. Every subsequent development in advertiser-funded media follows from this original structure.

The competition between William Randolph Hearst’s New York Journal and Joseph Pulitzer’s New York World in the 1890s shows what this model produces when two well-funded publishers compete intensely for circulation. Neither set out to destabilize democratic discourse; they just wanted to sell newspapers. However, the market provided a clear signal: crime, scandal, nationalist outrage, and stories written to produce emotional responses drove circulation, and each escalation by one paper forced a matching response from the other. Journalism that was inaccurate and inflammatory was not the result of a conspiracy. It was the predictable output of two rational businesses responding to the same incentive structure [Campbell2001,Nasaw2000].

The propaganda model [Herman1988] describes how this system shapes content without requiring deliberate censorship. Advertisers do not need to call editors and demand favorable coverage; they simply withdraw revenue from outlets that publish content they dislike, and editors learn what is acceptable without being told.

Interactive media doesn’t change any of this logic, but it makes it more precise. Behavioral data collected through social media, search engines, and app ecosystems allows advertisers to target individuals rather than demographic categories. The recommendation algorithm knows far more precisely what content will hold each specific user’s attention for the next few minutes than the newspaper editor deciding what to put on the front page.

Engagement maximization through algorithmic amplification and social harm are not separable because the content that maximizes engagement is not randomly distributed. Research consistently shows that outrage, fear, and social comparison generate stronger and more persistent engagement signals than accurate information, considered analysis, or content that simply satisfies a question and lets the user close the tab. Big tech companies that depend on advertising revenue will therefore always amplify inflammatory distortion over mere facts [Wu2016].

Big Tech is Like Multi-Level Marketing

Jay Van Andel and Rich DeVos founded Amway in 1959 on the premise that anyone with enough ambition and the right social network could build a business by selling cleaning products to friends and neighbors. The products were real; the business opportunity was considerably more complicated.

Amway is the founding institution of multi-level marketing (MLM), an industry that by the 2020s had enrolled an estimated 120 million people worldwide. The business model compensates them not just for selling products but for recruiting others who will also sell products, and for collecting a percentage of everything their recruits sell, in a chain extending downward through a downline.

The mathematics of this structure are simple, but tend not to appear in recruitment materials. If each participant recruits three others, and each of those recruits three more, then a chain seven levels deep involves over 2,000 people all of whom must sell product to sustain the commission structure above them. The US Federal Trade Commission found in a 2011 analysis that in one major MLM company, fewer than 1% of participants earned a net profit after expenses. The other 99% subsidized them [FitzPatrick2020].

When Uber launched, it told drivers they were entrepreneurs: captains of their own ships, free from the indignities of employment, with no boss, no fixed hours, and all the flexibility they wanted. What the pitch omitted was that Uber would set the price, determine which rides were offered to which drivers, deactivate accounts without meaningful appeal, and systematically reduce driver earnings as market penetration increased.

Amazon Marketplace allows third-party sellers to list products, reach Amazon’s enormous customer base, and pay Amazon a commission on every sale. It also allows Amazon to observe exactly which products are selling well and then launch competing Amazon Basics versions, using the sales data it collected from the sellers it hosts. Marketplace recruits more sellers by showcasing successful ones, in the same way that MLM recruitment materials feature the rare success story while omitting the statistical reality for the average participant.

Social media scales this model even further. Facebook’s users generate the content that makes Facebook worth visiting. They also generate the social ties that make Facebook difficult to leave. And they pay in attention and behavioral data for the privilege of generating that content on Facebook’s infrastructure under Facebook’s terms of service. The users are the product. This is not a metaphor: it is a statement of the actual business model that appears plainly in investor materials.

The biggest difference between MLM and social media is that MLMs are occasionally subject to regulatory action. The platform economy has largely avoided that outcome by being larger, more diffuse, and more politically connected. The most honest description of both is that they sell hope: the hope that this time, for this person, the math will work out [Srnicek2016].

Big Tech is Like the Yakuza

In the days immediately after the March 2011 Tōhoku earthquake and tsunami, investigators from the Asahi Shimbun documented how organized crime groups supplied food, water, and emergency goods to affected communities faster than official relief channels could mobilize. This was not unusual. The yakuza—Japan’s organized crime syndicates—have a long history of disaster relief, partly because it generates goodwill, partly because they maintain logistics networks and community ties that allow them to operate quickly when formal institutions cannot, and partly because disaster zones are also business opportunities.

Tech platforms now perform functions that governments once either provided directly or regulated others to provide, including identity verification, payment processing, and dispute resolution. When a seller on eBay disputes a transaction, eBay adjudicates the claim. When a developer’s app is removed from the App Store, Apple’s internal review process is the only available appeal. When Facebook removes content in a country with regulated speech, it is making regulatory decisions in a jurisdiction where it has not been granted regulatory authority.

This is what makes tech’s political relationships so interesting. Governments are simultaneously threatened by tech’s accumulation of quasi-governmental power and dependent on tech’s infrastructure to operate. The US government runs significant portions of its cloud operations on Amazon Web Services. The Indian government used WhatsApp—owned by Meta—for public health communications. The relationship is symbiotic in the same way that governments’ relationships with contractors always have been: the state needs services the contractor provides, the contractor needs the regulatory tolerance the state can provide, and neither has a strong interest in severing the arrangement.

The yakuza model also illuminates how platforms handle competition. Organized crime syndicates do not generally compete through price, but through territory. Territorial disputes are settled through negotiation, credible threats, and occasional violence. The enforcement mechanisms in tech may be different (so far), but the territorial logic is similar. Google defaults to Google Maps, Apple’s App Store prohibits payment systems that compete with Apple Pay, and Amazon uses its control of search ranking to disadvantage sellers who also list products on competing platforms. These practices are not illegal in most jurisdictions; they are exercises of territorial power by entities whose market position makes them difficult to challenge through normal competitive means.

So what do organized crime organizations provide in exchange for what they extract? The yakuza have historically managed significant portions of Japan’s construction and entertainment industries through a combination of legitimate business ownership and informal control over labor supply. The arrangement is not purely extractive: it provides predictability, dispute resolution, and protection from other organized crime groups. Platforms offer analogous services. Amazon Marketplace gives small sellers access to customers they could not otherwise reach. App Store review provides users a degree of protection from malware. Facebook Groups provide community infrastructure that many organizations genuinely depend on. The question that needs to be asked of both yakuza-connected industries and platform-dependent businesses is not whether the services have value, but whether the entity providing them has made itself structurally necessary specifically to extract rents that a competitive market would not sustain.

The yakuza (officially designated boryokudan in Japanese anti-crime legislation) are declining. Japan’s anti-organized crime laws, passed in 1992 and strengthened since, have made it progressively harder for syndicate members to interface with legitimate business. Banks will not open accounts for known members, real estate will not be rented to them, and golf courses are required to turn them away. Registered yakuza membership fell from roughly 180,000 in the 1960s to under 20,000 by the early 2020s.

None of this happened because the yakuza became less useful. It happened because a sustained political decision was made to make the cost of association with them prohibitive for legitimate businesses. The tech industry’s political connections are currently a source of strength; this history suggests they can be made a source of vulnerability [Adelstein2023,Kaplan2012].

Big Tech is Like the Radio Payola System

Supermarkets charge manufacturers slotting fees for shelf space, paying premium rates for eye-level placement and end-cap displays. A product placed at eye level is there because its manufacturer paid for the position, not because it is the best option in its category, but consumers have no way to know this.

In the 1950s and 1960s, record labels paid disc jockeys to play their records, presenting purchased airplay as their independent opinion. Congressional hearings in 1959 exposed the practice, the Federal Communications Commission made payola illegal, and Alan Freed, one of the most prominent DJs of the era, was prosecuted and his career ended [Segrave1994].

However, the hearings focused on individual wrongdoers rather than on the structure that made the wrongdoing rational. The practice therefore continued more or less uninterrupted through intermediaries that technically complied with disclosure requirements while achieving the same result. This is an example of regulatory capture: the agency charged with overseeing an industry becomes more responsive to the industry’s interests than to the public interest it was created to protect [Stigler1971].

A related problem arises when a platform operates as both a marketplace administrator and a competitor within that marketplace. Google places its own vertical search products at the top of search results while ranking competing services lower, and Apple controls the ranking system on the App Store that determines how visible its own applications and competing third-party applications are. The consumer’s position is identical to the radio listener in the payola era: prominent placement is a false signal of quality.

Securities law prohibits an analogous conflict known as front-running. A broker who executes customer orders is prohibited from trading against clients using client order information because taking advantage of insider information takes value from the customer. The standards that financial regulators apply have not been extended to platform markets, even though the identical conflict of interest produces the identical harm.

What would actually work is structural separation: a search engine that derived no revenue from placement would have no incentive to blur the line between paid and organic results. Short of this, accurate disclosure would require prominence and clarity that platforms have no incentive to provide, since their business model depends on users not knowing that the game is rigged [Doctorow2022,Stoller2019,Khan2017].

This is part of Version 2 of this material. See the whole series or the bibliography, or email me with feedback.