The Jagged Global Economy: Frontier AI Unevenly Exposes National Economies

Arul Murugan, Tomás Aguirre, Abhishek Nagaraj, Rishi Bommasani · arXiv (Cornell University) · 8 Jun 2026

Where this sits in the debate

Will AI destroy more jobs than it creates?

Direction of the finding as read from the abstract by an AI model (AI-written).

Key takeaway AI-written

The authors build a national AI exposure metric spanning 141 countries by combining occupation-level exposure scores with international employment data, finding high-income countries far more exposed than low-income ones (Europe/Central Asia 50% more exposed than Sub-Saharan Africa) and women more exposed than men in 91% of countries due to concentration in white-collar and sales work. They also introduce an indirect-exposure channel via remittances — Tajikistan's below-average direct exposure becomes above-average once its 37%-of-GDP dependence on Russian remittances is accounted for. This matters because it shows AI labor-market policy calibrated to US/European evidence will misfire elsewhere, and it validates the metric against Anthropic, Microsoft, and OpenAI adoption data.

The paper in three steps AI-written

Written by an AI agent from the paper's abstract, which is the primary source and is quoted below.

The question

How exposed are different national economies to frontier AI, given that AI's capabilities are uneven across tasks and countries employ people in different occupations?

The approach

The authors build a national AI exposure metric by combining occupation-level exposure scores with international employment data for 141 countries, validate it against AI adoption statistics published by Anthropic, Microsoft and OpenAI, and add indirect exposure through cross-country remittance dependencies.

The findings

High-income countries are substantially more exposed than low-income ones, and Europe and Central Asia are 50 percent more exposed than Sub-Saharan Africa. Women are more exposed than men in 91 percent of countries. Remittances can raise exposure: Tajikistan becomes above-average because 37 percent of its GDP is remittances from highly exposed Russia.

How to read it: observational. Analysis of real-world firm, worker, or market data — realistic settings; causal claims depend on the identification strategy.

Limitations and counterpoints AI-written

Exposure measures which jobs AI could affect, not realised effects on employment or wages. The occupation-level scores and the validation rely on available adoption statistics, which the abstract does not detail.

Abstract

Frontier AI's labor-market effects matter to workers, firms, and policymakers, but current evidence generally comes from a handful of high-income economies. The capabilities of frontier AI are jagged across work tasks and national economies diverge in how they allocate human labor. We introduce a national AI exposure metric that combines occupation-level exposure scores and international employment data for 141 countries. We find that high income countries are substantially more exposed than low income countries and that Europe and Central Asia are 50 percent more exposed than Sub-Saharan Africa. We also find a gender gap: women are more exposed than men in 91 percent of countries, driven by their concentration in white-collar and sales occupations. The exceptions are countries where women's employment remains concentrated in agriculture and household enterprises. We validate our national AI exposure estimates by showing they predict national AI adoption statistics published by Anthropic, Microsoft, and OpenAI. Beyond direct exposure, we identify a new mechanism for indirect exposure due to cross-country income dependencies. Some nations such as Tajikistan depend heavily on foreign workers remitting money back to their home countries: Tajikistan's direct exposure to frontier AI is below-average but because 37 percent of Tajikistan GDP is Russian remittance and Russia is very exposed, Tajikistan's remittance-accounted exposure becomes above-average. Our research shows that national variation in exposure is large enough that policy responses calibrated to U.S. or European labor markets will not generalize.

About this publication

Preprint. A preprint — posted publicly before or without journal peer review; findings may still be revised.

Published: 2026 (4 months ago)
Citations: 0 (OpenAlex)

0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison.