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

Simone Daniotti, Johannes Wachs, Xiangnan Feng, Frank Neffke

2026Science6 citationsDOI

Abstract

Generative coding tools promise big productivity gains, but uneven uptake could widen skill and income gaps. We train a neural classifier to spot artificial intelligence (AI)-generated Python functions in more than 30 million GitHub commits by 160,097 software developers, tracking how fast, and where, these tools take hold. Currently, AI writes an estimated 29% of Python functions in the US-a shrinking lead over other countries. We estimate that quarterly output, measured in online code contributions, consequently increased by 3.6%. AI seems to benefit experienced, senior-level developers: They increased productivity and more readily expanded into new domains of software development. By contrast, early-career developers showed no significant benefits from AI adoption. This may widen skill gaps and reshape future career ladders in software development.

Topics & Concepts

Python (programming language)Computer scienceGenerative grammarSoftwareArtificial intelligenceClassifier (UML)Source codeCoding (social sciences)Machine learningToolboxGenerative modelProductivitySource lines of codeSoftware engineeringDeep learningSoftware developmentArtificial neural networkArtificial Intelligence in Healthcare and EducationEthics and Social Impacts of AISoftware Engineering Research
Who is using AI to code? Global diffusion and impact of generative AI | Litcius