immiserating growth

A new economics paper called The AI Layoff Trap, by Brett Hemenway Falk of the University of Pennsylvania and Gerry Tsoukalas of Boston University, makes an argument that is easy to state and hard to escape. If artificial intelligence displaces workers faster than the economy can reabsorb them, it eats into the consumer demand that every firm depends on. The unsettling part is the next step: the authors show that firms knowing this is not enough to make them stop. Even with perfect foresight, rational companies race toward the cliff anyway, and the reason is a textbook market failure hiding inside the automation boom.

TLDR

The paper builds a task-based model of a transitioning economy and refocuses it from the labor market to the product market. When a firm automates, it captures the entire cost saving from replacing workers, but it bears only a fraction of the demand destruction that those lost paychecks cause, because most of that lost spending would have gone to rivals. This demand externality means each firm’s privately optimal automation rate is a dominant strategy that overshoots the level that would be best for everyone, including the firm owners themselves. Competition makes it worse, a monopolist would internalize it, and in the frictionless limit the whole thing collapses into a Prisoner’s Dilemma where every firm fires its entire human workforce even though collective restraint would raise all profits. Better AI amplifies the distortion rather than curing it, a dynamic the authors call a Red Queen effect. They test six policy responses. Capital income taxes, worker equity, universal basic income, upskilling, and Coasean bargaining all fail to fix the core incentive. Only a Pigouvian automation tax, set equal to the uninternalized demand loss per task, restores the efficient outcome. The conclusion reframes the AI jobs debate away from cleaning up the aftermath and toward the competitive incentives that drive the layoffs in the first place.

Thoughts

The cleverest move in this paper is where it points the camera. Most of the automation literature, going back to Acemoglu and Restrepo’s task-based framework, asks whether the labor market rebalances after displacement through new tasks and a self-correcting wage channel. Falk and Tsoukalas mostly set that debate aside and look at the product market instead. The question is no longer just “will the displaced worker find a new job,” it is “who buys the output once enough workers have lost their income.” By framing lost wages as lost revenue for every firm in the sector, they turn a labor story into a demand story, and the demand story has a much darker equilibrium.

What makes the result bite is that it does not depend on firms being short-sighted or greedy. The authors grant every firm perfect foresight. Everyone can see the demand cliff ahead. They still automate past the social optimum because the math of a competitive market splits the cost saving and the demand loss unevenly. You keep all the savings from firing your workers. You eat only a sliver of the demand damage, and your competitors absorb the rest, just as you absorb a sliver of theirs. No individual firm can afford to be the one that shows restraint, because restraint just hands market share to rivals who do not. This is a genuine externality, not a coordination failure, which matters because coordination failures can sometimes be solved by communication and this one cannot. Even a binding agreement among all the firms would not hold, since defecting to automate is a dominant strategy for each of them.

The Red Queen result is the part that should give AI optimists pause. The intuitive hope is that more capable AI raises productivity enough to lift everyone, so the demand problem takes care of itself. The model says the opposite. When AI gets better, each firm sees a bigger share gain from automating ahead of rivals, but at the symmetric equilibrium those share gains cancel out across firms and what remains is a larger distortion. Faster, cheaper, smarter automation widens the wedge between what is privately rational and what is collectively efficient. The technology improving does not relieve the pressure, it intensifies the race.

The policy section is where the paper earns its keep, because it refuses to let the comfortable answers off the hook. Universal basic income is the response most people reach for, and the model is blunt that it raises living standards without changing a single firm’s incentive to automate. It treats the symptom and ignores the margin. Upskilling and worker equity narrow the gap but cannot close it. Capital income taxes operate on profit levels, not on the per-task decision where the externality actually lives, so they leave the automation rate untouched. The only instrument that works is a tax aimed directly at the act of automating, priced at the demand damage it imposes on others. That is an uncomfortable conclusion for almost everyone. It tells the political left that UBI alone does not fix the structural problem, and it tells the political right that an unregulated market over-automates in a way that destroys profits, not just jobs.

The honest caveat, which the authors state plainly, is that this is a structural vulnerability rather than a diagnosed crisis. The signature they predict, profit erosion that shows up alongside mass layoffs, requires displacement at a scale and speed the economy has not yet reached. If reabsorption keeps pace, the externality stays too small to measure. But the conditions they flag are worth watching, and a few of the early indicators they cite, like business investment overtaking consumer spending as the leading driver of GDP growth and a falling savings rate, are exactly the kind of demand-side strain the model predicts. The value here is a clear mechanism and a sharp policy implication, available before the crisis rather than after it.

Key Takeaways

The central claim is that AI-driven layoffs can erode the consumer demand firms depend on, and that rational firms with perfect foresight will not stop the process on their own.
The mechanism is a demand externality. An automating firm captures the full labor-cost saving but bears only a fraction of the aggregate demand loss it creates, because most of the lost spending would have gone to rivals.
Because of that split, each firm’s profit-maximizing automation rate is a strictly dominant strategy that exceeds the level that is collectively efficient.
The resulting loss is not a transfer from workers to owners. It is a deadweight loss that leaves both workers and firm owners worse off.
The distortion deepens with competition. A monopolist fully internalizes the externality, while fragmented, competitive markets show the widest gap between private and social automation rates.
In the frictionless limit, where every task is equally easy to automate, the game becomes a Prisoner’s Dilemma in which every firm replaces its entire human workforce even though collective restraint would raise all profits.
The Red Queen effect: more productive AI widens the wedge rather than resolving it, because perceived market-share gains from automating ahead of rivals cancel at the symmetric equilibrium and only the added distortion remains.
Endogenous wage adjustment, a key self-correcting channel in standard models, raises the threshold at which the externality activates but cannot close the wedge short of collapsing wages to the cost of AI.
Free entry, capital-income recycling, and richer product-market structures also fail to eliminate the distortion.
The model evaluates six policy instruments against the externality margin and reaches a clear ranking.
Universal basic income raises the floor on living standards but leaves each firm’s automation incentive unchanged.
Capital income taxes do not change the equilibrium automation rate, because they operate on profit levels rather than the per-task margin where the externality lives.
Upskilling and worker equity participation narrow the wedge but cannot eliminate it.
Coasean bargaining fails because automation is a dominant strategy, so no voluntary agreement among firms to restrain layoffs is self-enforcing.
Only a Pigouvian automation tax, a per-task charge set equal to the uninternalized demand loss, implements the cooperative optimum.
The tax can be self-limiting. Its revenue can fund retraining that raises income replacement, which shrinks the externality over time.
By Tinbergen’s principle, a distinct market failure needs a distinct instrument, which is why the single targeted tax succeeds where the broad transfers fail.
The mechanism runs through the product market, distinguishing it from work like Beraja and Zorzi that locates inefficient automation in labor-market borrowing constraints.
Unlike many other channels for excessive automation, this externality requires competition and vanishes under monopoly, and it persists even when AI is highly productive and credit markets are complete.
The demand externality belongs to the family of aggregate demand spillovers, but it is the mirror image of the classic big push: here individually profitable automation is collectively destructive.
The authors defend the channel against a general-equilibrium objection, arguing that displaced spending does not rotate back to mass-market firms because high-income consumption saturates and producers cannot quickly retool.
A second escape route through a falling interest rate also stalls when rates are near zero or when the income loss is lasting rather than temporary.
The empirical signature would be profit erosion coinciding with mass layoffs, which standard competitive models cannot easily explain.
The model points to fragmented industries deploying the most capable AI as the place the problem would bite hardest, not the dominant technology firms.
Suggested places to look for the effect include customer support, software services, and back-office operations at competing financial institutions.
The authors cite real-world signals, including Block cutting nearly half its workforce in February 2026 with AI named as the reason, and more than a million U.S. job cuts announced in 2025 with AI explicitly tied to roughly 55,000.
They note that roughly 80% of U.S. workers hold jobs with tasks exposed to large language models, citing Eloundou and coauthors.
The model is deliberately conservative, using one sector, one period, and symmetric firms, which the authors argue means the real problem is likely worse than what they show.
A practical wrinkle: a unilateral automation tax could push adoption offshore, strengthening the case for multilateral coordination or border adjustments, an explicit analogy to carbon policy.
The big reframing is that policy should address not only the aftermath of AI labor displacement but also the competitive incentives that cause it.

Detailed Summary

A task-based model refocused on the product market

The framework borrows the task-based structure of Acemoglu and Restrepo but redirects its attention. Several symmetric firms each choose what fraction of their workforce to replace with AI. Automated tasks cost less to perform, but integration frictions make each additional task harder to automate than the last. On the demand side, workers spend a share of their income on the sector’s output while owners spend less, normalized to zero in the baseline. Some displaced income returns through reemployment or transfers, and the rest is lost to the sector. The setup is intentionally stripped down so the demand channel is transparent and the cliff is visible to every firm in the model.

The demand externality that traps every firm

Competition creates the trap. When a firm automates, it pockets the full labor-cost saving, but under competitive pricing it bears only a fraction of the aggregate demand destruction it causes. The rest spills onto rivals. Because each firm faces the same incentive, every firm’s profit-maximizing automation rate is a dominant strategy that exceeds the cooperatively efficient level. Foresight does not save them. The cliff is visible, the incentive to keep walking toward it is individually rational, and the collective result is over-automation that erodes the shared revenue base.

Competition deepens it, monopoly internalizes it

The size of the distortion depends on market structure. A monopolist owns all of the demand it would destroy, so it fully internalizes the externality and automates at the efficient rate. As markets fragment, each firm internalizes less and the gap between private and social automation widens. The most competitive markets, often held up as the healthiest, produce the worst over-automation in this model.

The frictionless limit becomes a Prisoner’s Dilemma

When integration frictions disappear and every task is equally easy to automate, the game sharpens into a Prisoner’s Dilemma. Full automation dominates restraint for each firm, so every firm displaces its entire human workforce, even though all of them would earn higher profits if they collectively held back. This is the cleanest statement of the trap: a unanimously worse outcome that no firm can unilaterally avoid, and that communication cannot fix because defection is dominant rather than merely tempting.

The Red Queen effect: better AI makes it worse

Higher AI productivity does not rescue the equilibrium. Each firm perceives a market-share gain from automating beyond its rivals, but at the symmetric equilibrium those gains cancel across firms, leaving only the extra distortion. So improvements in AI widen the wedge instead of closing it. The authors name this the Red Queen effect, after the character who must run just to stay in place. Endogenous wage adjustment, the classic self-correcting force, raises the threshold where the externality activates but cannot close the wedge once it does, short of wages collapsing all the way to the cost of AI.

Six policy fixes, and why only one works

The paper lines up six instruments against the externality. Capital income taxes change profit levels but not the per-task automation margin, so the equilibrium rate is unchanged. Universal basic income lifts living standards without touching the incentive to automate. Upskilling and worker equity narrow the wedge but leave a gap. Coasean bargaining cannot hold because automating is a dominant strategy, so no agreement is self-enforcing. Only a Pigouvian automation tax, set equal to the uninternalized demand loss per task, implements the cooperative optimum. Its revenue can fund retraining that raises income replacement, which shrinks the externality over time and can make the tax self-limiting. Tinbergen’s principle frames the lesson: a distinct market failure needs its own dedicated instrument.

Does the channel survive general equilibrium?

A natural objection is that in a frictionless multi-sector economy, displaced income would simply rotate to other spending and the mechanism would dissolve. The authors argue both escape routes are blocked for the mass-market firms most exposed to AI. Spending does not rotate back because high-income consumption saturates and mass-sector producers cannot quickly retool to capture redirected luxury demand. The other route runs through the interest rate: automation shifts income to owners who save more, raising aggregate saving, which a falling interest rate would normally recycle into investment. That adjustment stalls when rates are already near zero or when the income loss is lasting rather than temporary, so displaced workers cannot borrow their way through it.

What to watch for in the real economy

The distinguishing empirical signature would be profit erosion that shows up at the same time as mass layoffs, a combination standard competitive models struggle to explain since cost-cutting technology is supposed to raise profits. The authors are careful that this requires displacement at a scale and speed not yet reached, so the contribution is identifying a structural vulnerability rather than diagnosing an active crisis. They point to fragmented industries running the most capable AI as the place to look first, naming customer support, software services, and competing financial institutions’ back-office operations as concrete settings. They also flag a unilateral tax’s offshoring risk, drawing an explicit parallel to carbon policy and the case for multilateral coordination or border adjustments.

Notable Quotes

“At the limit, this becomes self-destructive: firms automate their way to boundless productivity and zero demand.”
The authors, framing the demand cliff that competitive automation runs toward.

“Rational, forward-looking firms should be the brake; if the cliff ahead is visible to all, why would they race toward it?”
The authors, setting up the puzzle the paper exists to answer.

“No firm can afford to be the one that holds back. This is the trap: an automation arms race that only intensifies as AI improves, that leaves workers and firm owners alike worse off, and that no market force can break.”
From the Discussion, stating the core result in plain language.

“Because over-automation leaves both firms and workers worse off, correcting it is a matter of eliminating waste, not of redistributing gains between them.”
The authors, on why the fix is not a left-versus-right transfer fight.

“This Red Queen effect means that ‘better’ AI, far from mitigating the externality, amplifies it.”
The authors, on why more capable AI deepens the distortion rather than curing it.

“The results suggest that policy should address not only the aftermath of AI labor displacement but also the competitive incentives that drive it.”
From the abstract, the paper’s central policy reframing.

You can read the full paper, including the formal propositions and the policy table, on arXiv here.

Tag: immiserating growth

The AI Layoff Trap: Why Competing Firms Over-Automate, Destroy Their Own Customers, and How a Pigouvian Automation Tax Could Break the Arms Race