How should Macroeconomists think about AI and the Labor Market?

As generative artificial intelligence (AI) moves rapidly from promise to practice, its early labor-market effects pose a puzzle for economists. Standard frameworks of skill-biased and routine-biased technological change predict that new technologies either complement highly skilled workers or displace routine labor. Yet emerging evidence paints a more complex picture. Recent results from Brynjolfsson et al. (2025a) show that the initial impact of widespread AI adoption is characterized by a pronounced contraction in employment-rather than wages-concentrated among highly educated, entry-level workers in prediction-intensive occupations, with little discernible effect on aggregate employment. We argue that these patterns cannot be fully understood within existing paradigms and call for a new conceptual synthesis. By integrating task-based macroeconomic models of automation with a microeconomic view of AI as a technology that substitutes for implementation skills while complementing human judgment, we delineate the backbone of a unified framework to analyze how AI is likely to reshape job design, reallocate comparative advantage across tasks, and alter the distributional consequences of technological change. This perspective serves to structure a formal research agenda on the evolving interaction between AI and labor markets and its implications for productivity, employment, and inequality.