Economic Scenarios for Transformative AI

Anton Korinek, Chad Jones, Szymon Sacher, Tess Cotter and Peter McCrory

No 21939, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: This paper presents a framework for assessing the economic consequences of AI between 2026 and 2030. In the model, AI automates a growing share of cognitive work, raising productivity and displacing workers who must search for jobs in other occupations. The model maps future paths of AI capabilities into implied paths for GDP, the labor share, wages, labor reallocation, and unemployment. We illustrate the framework by considering three scenarios: modest, substantial, and extreme. Under modest change, AI adds less than half a point to GDP growth by 2030 and raises unemployment by a tenth of a point. In the extreme change scenario, AI has transformative effects, with AI performing almost half of today's cognitive work by 2030. GDP growth then rises to 15 percent per year, the labor share of income falls from 60 to 45 percent, and nearly one in five cognitive workers is unemployed. We also surveyed US adults about their expectations for AI. Views vary widely, but the median respondent's answers are consistent with our substantial change scenario in which, by 2030, GDP rises by 8 percent and cognitive employment declines by 4 percent. The model offers a structured way to compare possibilities for our economic future under different expectations about AI.

Keywords: transformative AI; Automation; Labor share; Labor reallocation (search for similar items in EconPapers)
JEL-codes: E24 E25 J64 O33 O41 (search for similar items in EconPapers)
Date: 2026-09
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