The Competitive Trap: How AI-Driven Automation Creates Collective Market Failure

Jonathan Westover · Human capital leadership. · 1 Jul 2026

Where this sits in the debate

Will AI destroy more jobs than it creates? · its finding supports the claim (why studies differ)

Direction of the finding as read from the abstract by an AI model (AI-written).

Key takeaway AI-written

Westover formalizes a strategic externality in AI automation: citing over 100,000 tech layoffs attributed to AI in 2025, he uses game-theoretic models to show that each firm captures the full cost savings from automating while bearing only a fraction of the aggregate demand destruction, trapping rational firms in an automation arms race above the collectively optimal level, with the perverse results that better AI worsens the problem and fragmented markets suffer most. The policy claim is stark: wage flexibility, profit-sharing, and voluntary coordination cannot fix the distortion, so only Pigouvian automation taxes calibrated to uninternalized demand losses restore efficiency, sharpening the case for taxing the per-task automation margin rather than managing displacement after the fact.

The paper in three steps AI-written

Written by an AI agent from the paper's abstract, which is the primary source and is quoted below.

The question

Can competition push firms to automate with AI beyond the level that is best for workers and firm owners as a group?

The approach

Game-theoretic reasoning combined with recent observations on AI-related layoffs, focusing on how each firm keeps its cost savings while sharing the loss of consumer demand with others.

The findings

The article argues competitive pressure leads to more automation than is collectively optimal, that wage flexibility, profit-sharing or voluntary coordination do not remove this, and that taxes on automation set to the uninternalized demand loss could restore efficiency.

How to read it: theory. A theoretical model — its value is the mechanism it isolates; ask whether the assumptions fit the real economy.

Limitations and counterpoints AI-written

The abstract does not describe the empirical data behind its layoff figures or test the model against outcomes; the tax proposal is derived from the model rather than evaluated.

Abstract

Recent evidence suggests that artificial intelligence is displacing workers at an accelerating pace across multiple industries, with over 100,000 technology workers laid off in 2025 alone due to AI adoption. This article examines a critical yet underappreciated market failure: when firms automate in competitive environments, each captures the full cost savings while bearing only a fraction of the resulting demand destruction, creating a strategic externality that harms both workers and firm owners. Drawing on game-theoretic models and recent empirical observations, we demonstrate that competitive pressure traps rational, forward-looking firms in an automation arms race that exceeds collectively optimal levels. Neither wage flexibility, profit-sharing arrangements, nor voluntary coordination mechanisms can eliminate this distortion. Only policy interventions that directly address the per-task automation margin—specifically, Pigouvian automation taxes calibrated to uninternalized demand losses—can restore efficiency. The analysis reveals that "better" AI paradoxically amplifies rather than resolves the problem, and that fragmented markets suffer disproportionately. These findings suggest policy discourse should shift from managing displacement consequences to correcting the competitive incentives driving excessive automation.

About this publication

Academic journal. Published in an academic journal — typically peer-reviewed before publication, though indexing alone doesn't guarantee review depth.

Published: 2026 (4 months ago)
Citations: 0 (OpenAlex)