Market Beliefs about Open vs. Closed AI
Daniel Björkegren
TL;DR
This study investigates how market expectations respond to AI model releases, distinguishing between open-weight and closed (proprietary) models. Building on prior work, it uses an event-study framework to analyze long-term bond yields and other financial indicators around 47 model releases, with regression specifications that account for overlapping event windows and group-specific effects. The key finding is that yields move in opposite directions for open versus closed releases, with long-dated bonds declining after closed releases and rising after open releases, implying differential economic implications and a muted cumulative effect when pooling both types. The results suggest that market beliefs about diffusion, distribution of gains, and investment responses to openness play important roles, though the exact mechanisms remain uncertain and warrant further data as AI progress continues.
Abstract
Market expectations about AI's economic impact may influence interest rates. Previous work has shown that US bond yields decline around the release of a sample of mostly proprietary AI models (Andrews and Farboodi 2025). I extend this analysis to include also open weight AI models that can be freely used and modified. I find long-term bond yields shift in opposite directions following the introduction of open versus closed models. Patterns are similar for treasuries, corporate bonds, and TIPS. The different movements suggest that that markets may anticipate open and closed AI advances to have different economic implications, and that the cumulative impact of AI releases on bond yields may be more muted.