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.
Behavioral Intervention Construal: A Framework for Understanding Inferences from Behavioral Interventions
Managers and policymakers frequently use behavioral interventions—including incentives and messaging campaigns—to influence people’s behavior. They often choose an intervention by asking whether it will make a desired behavior easier, cheaper, or more attractive. Our research suggests they should also ask a second question: “What could this intervention unintentionally communicate?” For instance, interventions can signal that an organization is self-interested or trying to control people’s choices.
Dynamic Investment and Product Market Rivalry: The Network Q Model
We present a new dynamic model of corporate investment in imperfectly-competitive product markets, extending the neoclassical (Q) theory of investment to a multi-firm, multi-product, fully structural model. The model provides an explicit formula to quantify corporate investment and characterize investment spillovers for the entire network of firms in any economy.
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.