
I read a paper last week that made me put my laptop down and stare at the wall for a bit. Not because it said AI will take jobs, everybody says that now, and most of the time it's either panic bait or "learn to code" cope dressed up in a LinkedIn carousel. This was different. Two researchers at UPenn and Boston University built a formal game-theoretic model, ran the math, and proved something genuinely unsettling: even when every firm in a market knows that mass automation will destroy the consumer demand they all depend on, they do it anyway. Rationality doesn't save you. Perfect information doesn't save you. The structure of competition itself is the trap.

The paper is "The AI Layoff Trap" by Brett Hemenway Falk and Gerry Tsoukalas, published on arXiv in March 2026. And the reason it hit me is that I work with AI systems every day. I build them. I ship them. I've watched companies rip out entire teams and replace them with agents. And every time someone asks "won't this destroy the market eventually?", the implicit answer from management is always some version of "the market will adjust." This paper says: no, actually, it probably won't. Not on its own. And here's the math that proves it.

## the setup

The model is stripped down on purpose. You have N symmetric firms in a market. Each firm chooses what fraction α of its workforce to replace with AI. Automating a task saves money (the worker's wage minus the AI cost, call it s = w − c), but each successive task gets harder to automate because of integration friction (modeled as a quadratic cost k·α²/2). So far this is standard.

The twist is on the demand side. Workers are also consumers. When a firm lays off workers, those workers stop spending. A fraction λ of their wages was going to this sector's products, and only a fraction η of the lost income gets recovered through reemployment or transfers. So the demand loss per displaced worker is ℓ = λ(1−η)w. Every firm's revenue depends on aggregate demand, and aggregate demand depends on how many workers still have jobs.

Here's where competition creates the trap. When Firm A automates one more task, aggregate demand drops by ℓ·L (where L is the labor force). But competitive pricing splits revenue equally across N firms, so Firm A only feels ℓ·L/N of that demand destruction. The other (N−1)/N falls on rivals. Each firm captures the full cost saving but externalizes most of the demand damage. The firm isn't being irrational. It's being perfectly rational. And that's exactly the problem.

## the wedge

The paper defines two automation rates. The Nash Equilibrium rate αNE is what each firm actually does when maximizing its own profit. The Cooperative Optimum αCO is what a planner would choose if all firms coordinated to maximize joint profit. The gap between them is the over-automation wedge:

> αNE − αCO = ℓ(1 − 1/N) / k

This equation tells you three things immediately. First, a monopolist (N=1) has zero wedge because it internalizes all the demand loss. Second, the wedge grows with N, because more competitors means each firm bears a smaller share of the damage. Third, the wedge grows with ℓ (more demand destruction per layoff) and shrinks with k (more friction makes automation harder).

The really counterintuitive result is that more competition makes things worse. We usually think competition disciplines firms into acting in consumers' interests. Here, more competition dilutes each firm's share of the demand loss, which weakens the private incentive to restrain. The invisible hand is pushing everyone off a cliff.

Drag the sliders below and watch this happen in real time.

<AutomationWedgeExplorer />

Set N to 1 and the two lines overlap perfectly. Now drag it to 7 or 10. Watch the orange line (what firms actually do) pull away from the green line (what they should do). That gap is pure deadweight loss. Not a transfer from workers to owners. Both sides lose.

## the prisoner's dilemma

When you remove integration friction entirely (k → 0), the model collapses into something beautifully ugly: a classic Prisoner's Dilemma. Each firm faces a binary choice, automate everything or automate nothing, and full automation is the strictly dominant strategy even though mutual restraint would make everyone richer.

This isn't a coordination failure. Coordination failures are solvable, you just need firms to agree on which equilibrium to play. This is worse. Even if every CEO in the industry gets on a call and agrees to restrain, each firm's individually optimal move is still to defect. Communication is cheap talk. The agreement has no teeth because automating is dominant regardless of what rivals do.

<PrisonersDilemmaPayoff />

Look at the payoff matrix. Both firms at (100, 100) if they restrain. Both at something less than 100 if they automate. But if your rival restrains and you automate, you win big. And if your rival automates and you don't, you lose big. So you automate. Always. Even though you can see the cliff.

Block cut nearly half its 10,000-person workforce in February 2026. Jack Dorsey said AI had made those roles unnecessary and predicted most companies would reach the same conclusion within a year. Over 100,000 tech workers were laid off in 2025 with AI cited as the primary driver in more than half the cases. Salesforce replaced 4,000 customer support agents with agentic AI. This is not a hypothetical scenario from an econ paper. This is the Prisoner's Dilemma playing out in real time, with real people.

## so what fixes it?

This is where the paper gets genuinely useful. They evaluate six policy instruments against the externality. The results are sobering because most of the things people propose don't actually work on the right margin.

<PolicyDashboard />

Click through each one. The pattern is stark. Capital income taxes don't change the automation rate at all because a multiplicative tax doesn't alter the first-order condition, it just scales profits down uniformly. UBI raises the floor on living standards but doesn't change the per-task incentive to automate, firms still race. Coasian bargaining fails because automation is a dominant strategy, no voluntary agreement is self-enforcing. Upskilling and worker equity partially narrow the wedge but can't eliminate it.

Only the Pigouvian automation tax fully corrects the distortion. The idea is simple in principle: set the tax equal to the uninternalized demand loss per task, τ = ℓ(1−1/N). This makes each firm face the full social cost of its automation decision, not just its private share. The firm's first-order condition becomes identical to the cooperative planner's. And the tax revenue can fund retraining programs that increase η, which shrinks ℓ, which shrinks the required tax over time. The policy is potentially self-limiting.

## the red queen effect

There's one more result that kept bugging me. You might think that as AI gets better (lower cost c, higher productivity), the problem resolves itself because the cost saving s grows relative to the demand loss ℓ. The paper shows the opposite.

Higher AI productivity widens the wedge. Each firm sees a market-share gain from automating beyond rivals, but at the symmetric equilibrium these gains cancel out and leave only the additional distortion. It's a Red Queen effect: everyone runs faster and nobody gets ahead, but the collective damage accumulates. "Better" AI doesn't solve the externality. It amplifies it.

This is the part that should worry the "AI will create new jobs" optimists. The paper doesn't say new jobs can't emerge. It says even if they do (η > 0), the externality persists as long as displaced workers aren't landing in better-paying roles (η < 1). And the faster AI improves, the wider the gap between what firms do and what they should do.

## the math under the hood

Skip this if equations aren't your thing. But if you want the receipts, here they are.

The model has L workers, N symmetric firms, wage w, AI cost c per task. Each firm chooses automation rate αᵢ ∈ [0,1].

**Per-task economics:**
- Cost saving: s = w − c
- Demand loss per displaced worker: ℓ = λ(1−η)w
- Integration friction: quadratic k·α²/2

**Firm i's profit** (equation 6 from the paper):

> πᵢ = L · [s·αᵢ − (k·αᵢ²)/2 − (ℓ/N)·Σⱼαⱼ] + Π₀

where Π₀ is baseline profit with no automation.

**First-order condition** (private): ∂πᵢ/∂αᵢ = L · [s − ℓ/N − k·αᵢ] = 0, which gives us αNE = (s − ℓ/N) / k.

**Social planner's FOC** (faces full demand loss ℓ, not ℓ/N): αCO = (s − ℓ) / k.

**The wedge** falls out immediately: αNE − αCO = (s − ℓ/N)/k − (s − ℓ)/k = ℓ(1 − 1/N) / k.

**Automation threshold:** N* = ℓ/s = λ(1−η)w / (w−c). Firms automate only when N > N*. As AI costs fall (c → 0), N* → λ(1−η) ≤ 1, so the over-automation region expands to cover virtually any market with N ≥ 2.

**Generalized surplus loss** (Proposition 2): S(μ; αSP) − S(μ; αNE) = [(1−μ)·N·L·k / 2] · (αNE − αSP(μ))². This is quadratic in the wedge and scales with N·L, so both market fragmentation and market size amplify the welfare cost.

**Pigouvian tax correction:** Set τ = ℓ(1−1/N). The firm's new FOC becomes s − ℓ/N − τ − k·α = 0, which simplifies to s − ℓ − k·α = 0. That's exactly the cooperative planner's condition. The tax makes the private incentive match the social incentive.

**Frictionless Prisoner's Dilemma** (k=0): marginal profit becomes the constant L(s − ℓ/N). If positive (N > N*), full automation is strictly dominant. If s < ℓ, mutual automation yields Π₀ + L(s−ℓ) < Π₀. Total deadweight loss = N·L·(ℓ−s).

## my honest take

I build AI systems for a living. I've shipped agents that automate tasks people used to do manually. I know the cost savings are real, and I know the productivity gains are real. This paper doesn't say automation is bad. It says the incentive structure of competitive markets will push firms past the point where automation is collectively beneficial, and that's a structural problem you can't fix with goodwill, foresight, or voluntary agreements.

The thing that sticks with me is the Pareto dominance result. Over-automation doesn't just hurt workers. It hurts firm owners too. Both sides end up worse off at the Nash equilibrium than at the cooperative optimum. This isn't a story about greedy corporations. It's a story about a game where the rules punish everyone, including the people making the moves.

If you're in tech and you've been watching the layoff waves with a vague sense that "this can't keep going forever without consequences," now you have the formal model that explains why. The cliff is real, everyone can see it, and the structure of competition makes it rational to keep driving toward it. The paper says only a Pigouvian automation tax, set to the uninternalized demand loss, actually corrects the incentive. Everything else is either a band-aid or doesn't operate on the right margin.

I don't know if that tax is politically feasible. But at least now the problem has a name, a model, and a proof. That's more than we had last month 🫡


Two researchers proved mathematically that rational firms will over-automate with AI even when they know it destroys the demand they depend on. Only one policy instrument out of six actually fixes it.

Economics

The AI Layoff Trap. Why Knowing the Cliff Is Ahead Doesn't Stop the Race.