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

TL;DR

This paper analyzes the global adoption of generative AI coding tools, revealing uneven uptake that may widen skill gaps, with AI now generating a significant portion of code and influencing developer productivity and domain expansion.

Contribution

It introduces a neural classifier to detect AI-generated code in a large-scale dataset, providing new insights into the diffusion and impact of generative AI in software development.

Findings

AI generates 29% of Python functions in the US

Code contributions increased by 3.6% due to AI use

Experienced programmers benefit most, widening skill gaps

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 over 30 million GitHub commits by 170,000 developers, tracking how fast -- and where -- these tools take hold. Today, AI writes an estimated 29% of Python functions in the US, a modest and shrinking lead over other countries. We estimate that quarterly output, measured in online code contributions, has increased by 3.6% because of this. Our evidence suggests that programmers using AI may also more readily expand into new domains of software development. However, experienced programmers capture nearly all of these productivity and exploration gains, widening rather than closing the skill gap.