A new mathematical study warns that widespread artificial intelligence adoption could reshape the global economy. Researchers argue that unchecked automation may trigger a self-reinforcing cycle of job losses and weaker consumer demand. The peer-reviewed paper presents a theoretical economic model rather than a prediction. However, its findings have sparked fresh debate over AI’s long-term economic impact.
Economists Model AI-Driven Layoff Cycle
The study, titled “The AI Layoff Trap,” was published on March 2, 2026. Researchers from the Wharton School and Boston University authored the paper. They examined how firms could respond to rapid AI adoption in competitive markets.
According to the model, companies replace workers with AI systems to reduce operating costs. As competitors follow similar strategies, employment gradually declines across multiple industries. Consequently, fewer employed workers reduce overall consumer spending throughout the economy.
The researchers describe this process as a “layoff-demand feedback loop.” They argue that individually rational business decisions could collectively weaken economic growth. Therefore, firms may continue automating operations as demand keeps falling.
Study Suggests Automation Tax as Potential Solution
The researchers tested several policy responses within their economic model. However, most proposed measures only reduced the impact under the study’s assumptions. They concluded that these interventions could not fully stop the downward cycle.
Instead, the paper identifies a Pigouvian automation tax as the only complete solution within the model. The proposed tax would apply whenever companies replace human workers with AI systems. It aims to offset declining consumer demand caused by reduced employment.
The authors noted that no major economy currently uses such a policy. They also highlighted ongoing technology-sector layoffs alongside accelerating AI adoption. Nevertheless, they emphasized that their conclusions depend on the model’s assumptions rather than certain future outcomes.
