How LLMs could reshape unemployment theory by transforming job searches

Equilibrium unemployment theory finds itself at a fascinating crossroads with the rise of large language models. While economists have traditionally debated whether unemployment stems primarily from job search frictions or efficiency wage considerations, this distinction takes on new urgency as LLMs potentially transform labor markets. Understanding these competing explanations becomes crucial as we navigate a future where AI simultaneously reduces certain employment barriers while creating new challenges in hiring processes.

The big picture: Two competing economic theories attempt to explain why unemployment exists in equilibrium, with fundamentally different implications for how technological changes might affect labor markets.

• Search friction theory suggests unemployment exists because it takes time and effort for workers to find suitable jobs and for employers to find suitable workers.
• Efficiency wage theory proposes that employers deliberately pay above-market wages to attract better talent and motivate higher productivity, which necessarily creates unemployment as a disciplinary mechanism.

Why this matters: These competing theories predict opposite outcomes when job search frictions decrease, offering a natural experiment to determine which better explains real-world labor markets.

• If search frictions dominate, reducing the difficulty of finding jobs should decrease unemployment.
• If efficiency wages dominate, making job searches easier should paradoxically increase unemployment as employers need higher unemployment to maintain worker discipline.

The technological impact: Large language models are reducing certain job search frictions by making it easier to apply for positions, but this may create unexpected labor market dynamics.

• LLMs enable job seekers to generate customized applications rapidly, potentially reducing the time and effort needed to find suitable employment.
• The resulting flood of applications could create a new bottleneck on the employer side as hiring managers struggle to sort through increased volumes of candidates.
• This imbalance might actually increase unemployment if employers’ ability to process applications doesn’t keep pace with applicants’ ability to generate them.

The evidence: Historical data appears to support the search friction theory over efficiency wage explanations.

• When job search has become easier throughout history—through newspapers, telephones, the internet, and job boards—unemployment has generally decreased rather than increased.
• This pattern suggests search frictions, not efficiency wages, are the primary driver of equilibrium unemployment.

Reading between the lines: The efficiency wage theory, while elegant, likely describes secondary effects rather than the primary mechanism behind unemployment.

• While employers certainly use wages strategically, the disciplinary effect of unemployment appears less significant than the fundamental challenges of matching workers to appropriate jobs.

The bottom line: Resolving this theoretical debate has practical implications for how we should approach technological disruption in labor markets.

• As LLMs continue to evolve, developing equally powerful tools for employers to efficiently sort applications may become crucial to preventing technological unemployment.
• Understanding the true mechanisms behind unemployment will help policymakers design more effective interventions as AI continues transforming how we find and secure employment.