Cognitive Automation and Sustainable Development: Global Task‐Level Evidence on Large Language Models and Labor Markets

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a novel task-level framework to analyze LLM's impact on labor markets across countries.

🏢実務担当者:Use findings to anticipate workforce shifts in knowledge-intensive sectors and plan reskilling.

🏛政策担当者:Highlights the need for fair transition policies to balance productivity gains with employment equity.

The rise of large language models (LLMs) is transforming the trajectory of traditional rule‐based automation, while their global impact on the labor market remains largely unexplored. To assess this transition, we develop a novel bottom‐up framework linking detailed task data to occupational structures across a broad spectrum of economies. Our findings reveal that LLM‐driven automation disproportionately impacts roles centered on information processing, administration, and managerial coordination compared to those in physical or manual domains. At the sectoral level, this impact translates into higher exposure for knowledge‐intensive sectors like finance, education, and professional services, while sectors like agriculture and manufacturing remain more insulated. Because the industrial structure strongly determines a nation's vulnerability, economies reliant on administrative and clerical activities are facing greater exposure. Critically, potential productivity gains from this exposure are concentrated in areas contributing significantly to economic value rather than employment, highlighting a tension between efficiency and equitable labor outcomes. We argue that proactive policies, focusing on fair transitions, skill adaptation, and strategic industrial development, are crucial to avoid this technological shift undermining the sustainable development goals.

• semanticscholar https://doi.org/10.1002/sd.71238first seen 2026-06-05 05:37:07 · last seen 2026-06-16 05:18:07