AI Is Replacing Workers Faster Than It Should. The Mathematics Prove It.

A landmark paper from the University of Pennsylvania and Boston University mathematically proves rational companies can't stop themselves from automating too fast. History tells u

In the spring of 2026, Jack Dorsey announced that Block, the payments company he leads, had cut nearly half its 10,000-person workforce. Artificial intelligence, he stated publicly, had made many of those roles unnecessary, and he predicted that “within the next year, the majority of companies will reach the same conclusion”. It was not a boast. It read more like a warning issued to the world by someone who had already made his decision.

Dorsey’s announcement arrived against a backdrop that had been accumulating for months. According to data from Challenger, Gray & Christmas, nearly 55,000 U.S. job cuts in 2025 were directly attributed to artificial intelligence, out of a total 1.17 million layoffs. It is the highest annual figure since the pandemic. The technology sector led the pace, with positions in customer support, operations and middle management disappearing fastest. Salesforce replaced 4,000 customer-support agents with agentic AI. Goldman Sachs and Infosys deployed Cognition’s Devin system, enabling one senior engineer to handle what had previously required a team of five.

None of this was hidden from the executives making these decisions. That, it turns out, is precisely what makes it alarming.

The Architecture of a Trap

On 21 March 2026, two researchers published a paper on arXiv titled “The AI Layoff Trap”. Its authors were Brett Hemenway Falk of the University of Pennsylvania’s Department of Computer and Information Science and Gerry Tsoukalas of Boston University’s Questrom School of Business. Their conclusion was not a prediction or a policy recommendation. It was a formal proof: rational, perfectly informed companies cannot prevent themselves from automating beyond what is collectively optimal, and the mechanisms most commonly proposed to fix this problem do not work.

Falk leads Penn's Crypto and Society Lab, with research in cryptography and coding theory backed by NSF, DARPA, and IARPA. Tsoukalas is a professor and fellow across Boston University, Wharton, Cornell, and the Luohan Academy, with degrees from Stanford, MIT, and French institutions. Together, their complementary expertise produced a paper that is both technically rigorous and practically consequential.

The mechanism they describe works as follows. In a competitive market, each company can replace human workers with AI at lower cost per task. The company that automates gains a cost advantage; the company that does not is undercut. The logic of competition makes automation a dominant strategy and the optimal choice regardless of what rivals do. So far this is conventional. The twist is what happens to the workers once they are replaced because workers are also consumers. When they lose their income, they stop spending on the products and services those same companies sell. Each layoff slightly erodes the pool of consumer demand that all firms depend on. An individual firm captures the full cost saving from automating a task but, under competitive pricing, bears only a fraction of the resulting demand destruction while the rest falls on rivals.

This is what economists call an externality. It has the same structure as industrial pollution: a factory that dumps waste into a river saves money on disposal while the cost is distributed across everyone downstream. No individual factory has a financial reason to stop, even if collectively they are poisoning the water supply. Here, the equivalent of the river is consumer demand. At the limit, the authors note, firms automate their way to boundless productivity and zero demand. A monopolist would not fall into this trap, because a single firm internalises all the demand it destroys. The problem is structural to competition itself. More rivals means a wider gap between what firms do and what would be collectively optimal.

The authors model a frictionless version of this dynamic as a Prisoner’s Dilemma: every firm displaces its entire human workforce with AI, even though collective restraint would raise all profits. The resulting loss is not a transfer from workers to shareholders. It is a deadweight loss that harms both.

A further finding compounds the problem. The paper identifies what it calls a Red Queen Effect: as AI becomes more capable, the distortion grows rather than resolves. Better AI gives each firm a stronger incentive to automate beyond its rivals, but at the competitive equilibrium these relative gains cancel out, leaving only the additional demand destruction. The implication is that waiting for AI to improve its way out of this problem is precisely backwards. Progress accelerates the trap.

Six Fixes That Do Not Work

The paper tests six policy instruments against the externality margin, and the results are sobering.

i) Upskilling displaced workers reduces the damage without eliminating the competitive incentive to automate. ii) Worker equity participation in company profits narrows the wedge but leaves it open. iii) Coasian bargaining (voluntary agreements between firms and their workers) fails because automation remains a dominant strategy. No voluntary deal is self-enforcing when defection is always individually rational. iv) Capital income taxes operate on profit levels rather than on the per-task margin where the externality resides, leaving the automation rate unchanged. v) Universal basic income raises the floor on living standards but does not alter the incentive to automate.

vi) Only a Pigouvian automation tax corrects the problem at its source. Set equal to the uninternalised demand loss per task automated, such a tax makes each firm bear the cost it currently offloads onto rivals. The authors note that its revenue can fund retraining, which increases income replacement among displaced workers, which in turn reduces the size of the externality, which gradually reduces the tax required. Properly designed, the instrument is self-limiting.

What the Numbers Show

The theoretical trap Falk and Tsoukalas describe is already measurable in labour market data. The World Economic Forum’s Future of Jobs Report 2025, drawing on surveys of over 1,000 employers representing more than 14 million workers across 55 economies, projects that 92 million roles will be displaced globally by 2030 while 170 million new ones emerge. The net figure is a gain of 78 million jobs. Institutional projections from Goldman Sachs, McKinsey and the IMF broadly agree that the long-run aggregate effect of AI on employment is positive. That finding deserves to be taken seriously but it also deserves to be placed in context.

The International Monetary Fund estimated in 2024 that roughly 40 percent of jobs globally face meaningful exposure to AI capabilities, with that figure rising to 60 percent in advanced, digitised economies. Eloundou et al. (2024) found that approximately 80 percent of U.S. workers hold jobs with tasks susceptible to automation by large language models. McKinsey’s late-2025 research estimated that today’s technology could, in principle, automate approximately 57 percent of current U.S. work activities. These are not projections about the future. They describe what is technically possible now.

The demographic distribution of the risk is particularly notable. Data from Goldman Sachs Research and the SHRM 2025 Automation Survey found that 79 percent of employed U.S. women work in high-automation-risk occupations, compared to 58 percent of men. The clerical, administrative and customer service roles that AI is automating most aggressively are disproportionately held by women. Entry-level job postings have declined 15 percent year-over-year since the arrival of generative AI systems, suggesting that younger workers are being blocked from the career ladder at its first rung. Research by Brynjolfsson, Chandar and Chen (2025) found that since the release of ChatGPT, early-career workers aged 22 to 25 in the most AI-exposed occupations have faced systematically reduced hiring.

The net-positive long-run projections and the concentrated short-run pain are not contradictions. They describe the same phenomenon at different time scales. The WEF itself acknowledges that whether the net gain of 78 million jobs reaches displaced workers depends almost entirely on reskilling investment. Without it, the aggregate arithmetic is irrelevant to the individuals bearing the cost.

What History Teaches

The fear that technology will permanently destroy work is at least as old as the Industrial Revolution, as Falk and Tsoukalas note in their paper, citing Ricardo (1821), Keynes (1930) and Leontief (1982). Every major technological transition has vindicated the optimists in aggregate and vindicated the pessimists at the individual level. The task of understanding the current moment is to hold both truths simultaneously rather than choosing one for comfort.

The spinning jenny, the power loom and the steam engine mechanised textile production in Britain during the late 18th and early 19th centuries. Skilled handloom weavers, who had commanded some of the highest wages in the craft economy, were reduced to poverty within a generation. The Luddite rebellions of 1811 to 1813 were not a rejection of technology as such but a desperate protest against the erasure of a skilled trade without any replacement or support. A 2022 study by Bengtsson, van Maarseveen and Poignant, using archival data from the 19th-century Swedish iron industry linked to census records, found that workers displaced by industrial transformation ended up in occupations paying on average 10 percent less than their pre-displacement wages. The hardship persisted throughout their working lives.

The economic historian Robert Allen documented what the aggregate figures obscured: even as output per worker rose during the early Industrial Revolution, real wages stagnated. Wages did not begin rising in line with productivity until the mid-19th century. The gains, when they came, were extraordinary. But the transition consumed the working lives of those who endured it.

The United States encountered a version of this dynamic at scale during the Great Depression, when the mechanisation of agriculture and early manufacturing automation compounded a financial collapse that left 25 percent of the workforce unemployed with no social safety net in place. President Franklin Roosevelt’s response was not to halt automation. It was to build institutions capable of distributing its costs and its gains more broadly. The Works Progress Administration employed 9 million people before it was disbanded in 1943. The Social Security Act of 1935 created unemployment insurance, old-age pensions and disability coverage. The National Labor Relations Act guaranteed collective bargaining. Roosevelt’s Secretary of Labor, Frances Perkins, described the Act’s significance in terms that have not aged: it “reversed historic assumptions about the nature of social responsibility” and established that the individual holds social rights that do not evaporate with a job.

Post-war reconstruction produced, in many advanced economies, the most broadly shared prosperity in modern history. A combination of strong union representation, high marginal tax rates on capital, robust public investment in education and a political settlement between labour and management kept the displacement and reinstatement effects roughly in balance. The economist Nicholas Kaldor documented the resulting stability of labour’s share of national income a balance that rested on new task creation offsetting the automation of existing ones.

That balance has been fraying since at least the 1980s. Research by Autor et al. (2024) finds that the pace of displacement has intensified over four decades while the creation of genuinely new work has not kept pace. The "reinstatement effect" that stabilised previous transitions has weakened. Real wages for median workers in most advanced economies are broadly flat, and the gig economy of the 2010s stripped away the protections that had cushioned earlier technological transitions without offering replacements.

The Policy Debate

Against this backdrop, several responses have gathered momentum, though none has yet been implemented at the scale the problem requires. Universal basic income is the most discussed. The Stanford Basic Income Lab has tracked 163 UBI pilot programs in the United States alone, 41 of them still active. Cook County, Illinois, permanently expanded a guaranteed income initiative in its 2026 budget, providing $500 per month to 3,200 households; 94 percent of recipients used the funds to address financial crises and 70 percent reported improved mental health. Ireland launched what it described as the world’s first permanent Basic Income for the Arts program in 2026. Separately, the UK government’s minister for investment, Lord Jason Stockwood, told the Financial Times in early 2026 that the government was weighing UBI as a mechanism to support workers displaced by AI, and had previously floated funding it through taxes on technology companies.

The Falk-Tsoukalas paper is direct about UBI’s limitations. It raises the floor for displaced workers and, crucially, preserves the consumer demand that firms depend on. It does not, however, alter the competitive incentive to automate. A firm facing an automation tax assesses whether the cost of replacing a worker exceeds the benefit. A firm facing a UBI-funded safety net has the same cost-benefit calculation it had before. The paper’s conclusion is that UBI and a Pigouvian automation tax are not alternatives. They are complements, serving different functions in a coherent policy architecture.

The reskilling question has attracted more employer commitment, at least on paper. The World Economic Forum’s 2025 survey found that 77 percent of employers plan to fund reskilling programs through 2030. A PwC AI Jobs Barometer of 2025 found that workers in AI-exposed sectors who do reskill could earn a 56 percent wage premium. Against those intentions, ManpowerGroup’s Global Talent Barometer for 2026 found that 56 percent of workers globally have received no AI training at all. The gap between what employers plan and what workers are experiencing is one of the defining features of the current transition.

Geoffrey Hinton, the Nobel-winning physicist whose foundational work on neural networks helped create the technology now reshaping the labour market, cautioned the Financial Times that the distribution of AI-generated gains was the central problem. “It is going to create massive unemployment and a huge rise in profits,” he warned. “It will make a few people much richer and most people poorer. That is not AI’s fault. That is the capitalist system.” His diagnosis maps precisely onto the mechanism Falk and Tsoukalas formalised: the gains from automation accrue to those who own the technology; the costs are distributed across those who formerly provided the labour, and across the rival firms whose revenue base shrinks.

“It is going to create massive unemployment and a huge rise in profits. It will make a few people much richer and most people poorer. That is not AI’s fault. That is the capitalist system.” Geoffrey Hinton, Nobel Laureate in Physics (2024), speaking to the Financial Times

Scholars at the London School of Economics, writing in the LSE Business Review in April 2025, argued that income support alone is insufficient and that what is required is a new social contract in which technological progress and human welfare advance together rather than at each other’s expense. That framing connects the immediate policy debate to the deeper historical pattern. The new deal did not merely redistribute money. It rebuilt the institutional relationship between the state, the economy and the individual. The question now is whether the current scale of disruption is sufficient to catalyse a comparable institutional response before the costs have been fully borne by those least equipped to absorb them.

The Signal in the Proof

What distinguishes the Falk-Tsoukalas paper from most contributions to this debate is its refusal of ideology. It does not argue that automation is bad, that companies are greedy or that markets have failed in some generalised sense. It shows, through a formal model, that a specific and correctable market failure produces a specific and measurable distortion that harms shareholders and workers alike. The policy implication follows from the logic of the proof rather than from any prior political commitment.

That specificity matters. It moves the conversation from the question of whether AI will displace workers (a question the data has substantially answered) to the question of whether the competitive dynamics of AI adoption will produce more displacement than is necessary or efficient. The paper’s answer is that they will, unavoidably, unless a corrective mechanism is introduced at the point where the externality is generated.

The historical record offers grounds for measured confidence that advanced economies can navigate major technological transitions without permanent harm to aggregate living standards. It offers no grounds for confidence that the transition will be painless, equitable or self-correcting. Every precedent in which the eventual outcome was broadly positive involved deliberate institutional intervention: labour law reform, public investment, social insurance, collective bargaining rights or some combination of them. The transitions in which those interventions were absent or delayed produced decades of concentrated suffering that the aggregate statistics, looked at later, did not fully capture.

The trap Falk and Tsoukalas have identified carries one further implication that the debate has largely ignored. The corrective window is not fixed. As AI capability improves and successive model generation expands the range of tasks that can be automated cheaply, the wedge between what firms do and what would be collectively optimal widens. The authors are precise on this point: better AI amplifies the distortion rather than resolving it. Jack Dorsey predicted in February that most companies would reach his conclusion within the year. If he is right, the moment at which a Pigouvian tax could be set at a rate sufficient to close the wedge without triggering a disorderly adjustment is not some abstract future horizon. It is now, or it is harder.