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arXiv · 2511.02752

AI Diffusion in Low Resource Language Countries

Amit Misra·Syed Waqas Zamir·Wassim Hamidouche·Inbal Becker-Reshef·Juan Lavista Ferres
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Abstract

Artificial intelligence (AI) is diffusing globally at unprecedented speed, but adoption remains uneven. Frontier Large Language Models (LLMs) are known to perform poorly on low-resource languages due to data scarcity. We hypothesize that this performance deficit reduces the utility of AI, thereby slowing adoption in Low-Resource Language Countries (LRLCs). To test this, we use a weighted regression model to isolate the language effect from socioeconomic and demographic factors, finding that LRLCs have a share of AI users that is approximately 20% lower relative to their baseline. These results indicate that linguistic accessibility is a significant, independent barrier to equitable AI diffusion.

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Amit Misra, Syed Waqas Zamir, Wassim Hamidouche, Inbal Becker-Reshef, Juan Lavista Ferres. 2025-11-04. AI Diffusion in Low Resource Language Countries. https://arxiv.org/abs/2511.02752

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