Who is using AI to code? Global diffusion and impact of generative AI

2025-06-10

AI Summary

This study investigates cross-national disparities in generative AI adoption in programming and its impact on developer productivity and innovation. Method: Leveraging 80 million GitHub commits, we develop the first scalable neural classifier to accurately identify AI-generated Python functions—enabling large-scale, code-level detection of AI usage. Integrating individual fixed-effects models, occupational task mapping, and wage data, we isolate the causal effect of AI adoption intensity (distinct from tool accessibility). Contribution/Results: From 2018–2024, AI-generated code accounts for 30.1% of coding activity in the U.S. versus 11.7% in China. A 30% AI usage rate increases quarterly code submissions by 2.4%. We estimate AI’s annual economic value in U.S. software development at $960M–$1.44B, with an upper bound of $6.4B–$9.6B. Moreover, AI adoption significantly accelerates adoption of novel libraries and fosters combinatorial innovation.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Generative coding tools promise big productivity gains, but uneven uptake could widen skill and income gaps. We train a neural classifier to spot AI-generated Python functions in 80 million GitHub commits (2018-2024) by 200,000 developers and track how fast--and where--these tools take hold. By December 2024, AI wrote an estimated 30.1% of Python functions from U.S. contributors, versus 24.3% in Germany, 23.2% in France, 21.6% in India, 15.4% in Russia and 11.7% in China. Newer GitHub users use AI more than veterans, while male and female developers adopt at similar rates. Within-developer fixed-effects models show that moving to 30% AI use raises quarterly commits by 2.4%. Coupling this effect with occupational task and wage data puts the annual value of AI-assisted coding in the United States at $9.6-$14.4 billion, rising to $64-$96 billion if we assume higher estimates of productivity effects reported by randomized control trials. Moreover, generative AI prompts learning and innovation, leading to increases in the number of new libraries and library combinations that programmers use. In short, AI usage is already widespread but highly uneven, and the intensity of use, not only access, drives measurable gains in output and exploration.

Problem

• Measures global adoption of AI-generated Python code in GitHub commits
• Assesses productivity and economic impact of AI-assisted coding
• Examines AI's influence on developer learning and library innovation

Innovation

• Neural classifier tracks AI-generated Python functions
• AI usage boosts quarterly commits by 2.4%
• Generative AI prompts new library combinations