AI Happens

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.