Abstract

This report examines how Claude AI is reshaping the global economy by introducing five foundational "economic primitives", simple measures capturing task complexity, human and AI skill levels, use case categories (work, education, personal), AI autonomy, and task success rates. Analyzing 1 million anonymized conversations from Claude.ai and enterprise API traffic (November 2025), the study reveals striking geographic variation in AI adoption and usage patterns.

Key findings

Key findings indicate that Claude remains concentrated on coding-related tasks (34% of Claude.ai usage, 46% of API traffic), though usage is becoming more evenly distributed across US states, with potential parity achievable in 2-5 years. Takeaways: Prompting is still critical, AI can handle massive tasks if you break them Up, and deskilling is a bigger issue than unemployment.

Globally, GDP per capita strongly predicts AI adoption (0.7% increase per 1% GDP increase), while coursework use dominates lower-income countries and personal use is higher in wealthier nations. AI demonstrates greater productivity gains on higher-education tasks (12x speedup for college-level work vs. 9x for high school level), but success rates decline with task complexity (66% for college-level vs. 70% for basic tasks).

Notably, removing tasks Claude can perform would produce a net deskilling effect across most occupations, as AI tends to automate the more skill-intensive components of jobs. Adjusted for task reliability, labor productivity growth implications are revised downward from 1.8 to 1.0-1.2 percentage points annually over the next decade, with significant variation depending on task complementarity assumptions.

The study concludes that AI's economic impact will be geographically uneven and mediated by existing institutional structures, requiring development of human capital to ensure equitable global benefits.