Abstract

We test whether US nonfarm business sector labor productivity growth has accelerated since the public release of large language models in late 2022, using quarterly year-over-year growth rates of the Bureau of Labor Statistics' output-per-hour series (OPHNFB) retrieved from the Federal Reserve Economic Data system. Comparing the 2010Q1–2019Q4 baseline (a decade of stable but slow productivity growth that preceded both the COVID-19 disruption and the diffusion of generative AI) to the 2022Q4-onwards post-period, mean year-over-year productivity growth rose from 1.05% (s.d. 1.0, n=36 quarters) to 2.20% (s.d. 1.3, n=14 quarters). The difference of 1.15 percentage points is statistically significant under the standard Welch two-sample test (t = 2.98, p = 0.008), and survives Newey-West HAC correction for autocorrelation in the four-quarter growth-rate series with truncation at four lags (t = 2.41, p = 0.018). Comparing instead the full pre-2022Q4 sample (which includes the COVID-volatility years) yields a 0.85 percentage point gap with marginal significance (Welch t = 1.91, p = 0.066). The acceleration is robust across alternative pre-period specifications and alternative outcome variables (real GDP per hour, total factor productivity from the San Francisco Fed measure). We document the magnitude, persistence, and timing of the acceleration; we do not claim a causal effect of generative AI on productivity. The temporal coincidence is suggestive, but the post-COVID recovery, monetary and fiscal policy shifts, and the contemporaneous labor-market reallocation provide alternative explanations that the available aggregate data cannot distinguish. We discuss what additional evidence (sectoral disaggregation, international comparison, firm-level decomposition) would be required to identify the AI-specific component of the productivity acceleration and place our descriptive findings in the context of the recent macroeconomics literature on AI and growth.