Artificial Intelligence and Labor Market Reallocation [pdf] (conference.nber.org)

A recent paper by HieJoo Ahn and Nicholas A. Carollo examines the profound impact of artificial intelligence (AI), particularly large language models (LLMs), on the U.S. labor market. By integrating traditional labor market data with AI exposure and adoption metrics, the authors reveal that since the introduction of LLMs, workers in high AI-exposure roles have seen significant declines in job-finding and job-switching rates. This trend suggests that AI is reshaping hiring practices, increasing task reorganization within firms while simultaneously weakening demand for high-exposure workers. The study estimates that the natural rate of unemployment has increased by approximately 0.1–0.2 percentage points since LLMs became prevalent, indicating a rise in reallocation pressure affecting workers’ ability to transition to sectors with better employment prospects. This research is significant for the AI/ML community as it highlights the dual-edged nature of AI adoption—while it can enhance productivity, it also introduces complexities such as displacement and increased labor market frictions. The findings underscore the need for policymakers to address these challenges, as ongoing uncertainty about AI capabilities contributes to weaker hiring trends, particularly in high-exposure sectors. By providing the first comprehensive analysis of AI's effects on labor market dynamics, including unemployment and job flow statistics, this paper contributes valuable insights into how AI reallocation pressures are reshaping employment landscapes.