How Early Adopters Used Generative AI Worldwide: Variation by Country Income and Language

2026-05-28

AI Summary

This study investigates whether the global early adoption of generative AI exacerbates or mitigates the digital divide, with a focus on how national income levels and linguistic backgrounds shape usage patterns. Leveraging large-scale, anonymized chatbot interaction data linked to country-level GDP per capita and language metrics, the research employs cross-national socioeconomic association modeling and multilingual usage pattern analysis. It provides the first empirical evidence at a global scale that users in low-income countries predominantly employ AI for educational purposes, whereas those in high-income countries use it more for leisure. Furthermore, English usage is disproportionately overrepresented among non-English-speaking countries. The findings underscore the critical role of multilingual models in advancing technological equity and narrowing the digital divide.

Abstract

AI is being used by people globally, but not everyone is using it in the same ways. Using a large-scale dataset of anonymized, de-identified, and privacy-scrubbed interactions with a widely available and free AI chatbot, we empirically characterize differences in early adopters' usage across countries. Schooling is the most common domain of use in most countries, particularly low-income countries, with a strong inverse association evident between schooling and country-level GDP. Leisure-related use, by contrast, is positively associated with country-level income. Language, we find, also shapes use: English-language interactions are overrepresented in places where the predominant languages were not well-served by existing models during the period of the study. Improving performance across languages may be a key factor, our work suggests, in whether this technology expands digital divides or enables leapfrogging.

Problem

• generative AI
• digital divide
• country income
• language
• AI adoption

Innovation

• generative AI
• global usage patterns
• digital divide
• language bias
• early adopters