Measuring AI Diffusion: A Population-Normalized Metric for Tracking Global AI Usage

TL;DR

This paper introduces AI User Share, a population-normalized metric derived from Microsoft telemetry, to track and analyze global AI adoption patterns across 147 economies, revealing disparities and trends in AI diffusion.

Contribution

The paper presents a novel, real-time, population-normalized AI usage metric that enables cross-country comparison and insights into global AI diffusion.

Findings

Wide variation in AI adoption across countries

Strong correlation between AI User Share and GDP

Substantial latent demand in lower-income countries

Abstract

Measuring global AI diffusion remains challenging due to a lack of population-normalized, cross-country usage data. We introduce AI User Share, a novel indicator that estimates the share of each country's working-age population actively using AI tools. Built from anonymized Microsoft telemetry and adjusted for device access and mobile scaling, this metric spans 147 economies and provides consistent, real-time insight into global AI diffusion. We find wide variation in adoption, with a strong correlation between AI User Share and GDP. High uptake is concentrated in developed economies, though usage among internet-connected populations in lower-income countries reveals substantial latent demand. We also detect sharp increases in usage following major product launches, such as DeepSeek in early 2025. While the metric's reliance solely on Microsoft telemetry introduces potential biases related to this user base, it offers an important new lens into how AI is spreading globally. AI User Share enables timely benchmarking that can inform data-driven AI policy.