AI Diffusion in Low Resource Language Countries
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.
Cite
@article{arxiv.2511.02752,
title = {AI Diffusion in Low Resource Language Countries},
author = {Amit Misra and Syed Waqas Zamir and Wassim Hamidouche and Inbal Becker-Reshef and Juan Lavista Ferres},
journal= {arXiv preprint arXiv:2511.02752},
year = {2025}
}
Comments
9 pages, 4 tables. Also available at https://aka.ms/AI_Diffusion_Low_Resource_Language_Countries