The Stabilisation Gap: Artificial Intelligence Exposure and Macroeconomic Policy Space in the World's Ten Largest Economies

The 2026 Prize in Economic Sciences recognised Robert E. Hall and Michael Woodford for the microeconomic foundations of macroeconomic stabilisation. This article asks what their framework implies for artificial intelligence (AI) in the ten largest economies by 2026 nominal GDP (IMF World Economic Outlook, October 2026): the United States, China, Germany, Japan, the United Kingdom, India, France, Russia, Italy and Brazil. I build a country index of occupational AI exposure from ILOSTAT employment by ISCO-08 major group and the AI Occupational Exposure scores of Felten, Raj and Seamans, and combine it with Stanford HAI capacity data, the IMF AI Preparedness Index, IMF fiscal data and BIS policy rates. Three findings emerge. First, AI capacity is far more concentrated than output: the United States and China produce 64% of the group's GDP but account for 90% of its AI private investment, and occupational exposure is unrelated to AI capacity (Spearman ρ = 0.02, exact permutation p = 0.97). Second, two pre-specified hypotheses are not supported: the most exposed economies do not have systematically less fiscal or monetary space (ρ = 0.14, p = 0.71), and lagged AI investment does not predict labour-productivity growth in 2018–2025 (randomisation p = 0.86). Third, a calibrated exercise based on the consumption Euler equation shows that, when the intertemporal elasticity of substitution is below one, an AI-driven rise in trend growth raises the interest–growth differential. With an elasticity of 0.5 and the mid-range OECD productivity scenario, the debt-stabilising primary balance rises by 1.1 percentage points of GDP in Japan and by 0.7–0.8 points in the United States, Italy and France. The gain would widen monetary room above the lower bound while narrowing fiscal room where public debt is highest. Replication data and code: https://doi.org/10.5281/zenodo.23350759