Data and methods for identifying artificial intelligence-related patents
This paper evaluates existing approaches to identifying artificial intelligence (AI)-related patents and introduces a novel, scalable framework to improve classification performance. Motivated by the growing reliance on patent data in innovation research, we assess widely used methods and document their substantial performance limitations. To address these challenges, we develop a CPC-informed label-refinement framework inspired by positive-unlabeled (PU) learning to construct high-quality training data.
Understanding Firms’ AI Efforts and Their Economic Impact
Our research asks a central question for businesses investing heavily in artificial intelligence: Is AI actually making firms more productive, and if so, how? Using 15 years of data on AI-skilled employment at U.S. public firms, we find that firms increasing their AI investments experienced significantly faster productivity growth from 2018 to 2024—roughly one percentage point of additional productivity growth per year for a one-standard-deviation increase in AI investment. Importantly, these gains do not appear immediately; they build gradually over several years.
Three Strategic Bets on AI’s Future
This paper examines competition in the consumer AI assistant market using worldwide iOS and Android app-store data from seven major AI assistants from May 2023 through December 2025. Rather than finding a winner-take-all market, we show that major product launches tend to coincide with growth in the overall category, with little evidence of direct cannibalization across leading models. In other words, the “AI war” appears less zero-sum than commonly assumed.
The Caring Machine: Feeling AI for Customer Care
Feeling skills have always been a human domain, but AI is advancing rapidly. This article shows how AI can be used to develop feeling intelligence and build empathetic customer relationships.