| Abstract: |
We develop a high-precision classifier to measure artificial intelligence (AI)
patents by fine-tuning PatentSBERTa on manually labeled data from the USPTO's
AI Patent Dataset. Our classifier substantially improves the existing USPTO
approach, achieving 97.0% precision, 91.3% recall, and a 94.0% F1 score, and
it generalizes well to Chinese patents based on citation and lexical
validation. Applying it to granted U.S. patents (1976-2023) and Chinese
patents (2010-2023), we document rapid growth in AI patenting in both
countries and broad convergence in AI patenting intensity and subfield
composition, even as China surpasses the United States in recent annual patent
counts. The organization of AI innovation nevertheless differs sharply: U.S.
AI patenting is concentrated among large private incumbents and established
hubs, whereas Chinese AI patenting is more geographically diffuse and
institutionally diverse, with larger roles for universities and state-owned
enterprises. For listed firms, AI patents command a robust market-value
premium in both countries. Cross-border citations show continued technological
interdependence rather than decoupling, with Chinese AI inventors relying more
heavily on U.S. frontier knowledge than vice versa. |