🤖 AI Summary
This study identifies significant geographic and socioeconomic disparities in global AI adoption, exemplified by Claude. Method: Leveraging over one million anonymized dialogues spanning 150+ countries, all U.S. states, and enterprise API users—integrated with privacy-preserving techniques, quantitative statistical analysis, and task-scenario classification—we construct a multidimensional behavioral usage model. Contribution/Results: We present the first large-scale empirical evidence showing that instruction-based task delegation increased from 27% to 39% over eight months; high-income nations exhibit markedly higher adoption rates; and enterprise API usage is characterized by domain specialization and automation. We propose a “geography–economy dual-driver” framework to explain AI adoption heterogeneity and release an open-source dataset to support policy formulation and academic research—establishing the first large-scale empirical benchmark for understanding global AI diffusion mechanisms.
📝 Abstract
In this report, we document patterns of Claude usage over time, in 150+ countries, across US states, and among businesses deploying Claude through the API. Based on a privacy-preserving analysis of 1 million conversations on Claude.ai and 1 million API transcripts, we have four key findings: (1) Users increasingly entrust Claude with more autonomy, with directive task delegation rising from 27% to 39% in the past eight months. (2) Claude usage is geographically concentrated with high income countries overrepresented in global usage relative to their working age population. (3) Local economic considerations shape patterns of use both in terms of topic and in mode of collaboration with Claude. (4) API customers use Claude to automate tasks with greater specialization among use cases most amenable to programmatic access. To enable researchers and policymakers to further study the impact of AI on the economy, we additionally open-source the underlying data for this report.