More Analogies

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].