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

arXiv: https://arxiv.org/abs/2506.08945 · HTML: https://mlsanity.com/paper/2506.08945/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2025.

## Built on by (most cited first, 18 of 18)

- [Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects](https://mlsanity.com/paper/2511.04427/) (2025)
- [The Impact of AI-Generated Text on the Internet](https://mlsanity.com/paper/2604.26965/) (2026)
- [Vibe Coding in Product Teams: Reconfiguring AI-Assisted Workflows, Prototyping, and Collaboration](https://mlsanity.com/paper/2509.10652/) (2025)
- [Chinese Language Is Not More Efficient Than English in Vibe Coding: A Preliminary Study on Token Cost and Problem-Solving Rate](https://mlsanity.com/paper/2604.14210/) (2026)
- [GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing](https://mlsanity.com/paper/2605.27360/) (2026)
- [Minimal Prompt Perturbations Lead to Code Vulnerabilities: Prompt Fragility and Hidden-State Signals in Coding LLMs](https://mlsanity.com/paper/2605.29737/) (2026)
- [Breaking Memorization Barriers in LLM Code Fine-Tuning via Information Bottleneck for Improved Generalization](https://mlsanity.com/paper/2510.16022/) (2025)
- [Extracting Recurring Vulnerabilities from Black-Box LLM-Generated Software](https://mlsanity.com/paper/2602.04894/) (2026)
- [LLMs Generate Kitsch](https://mlsanity.com/paper/2604.25929/) (2026)
- [Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring](https://mlsanity.com/paper/2605.00754/) (2026)
- [Adoption and Impact of Command-Line AI Coding Agents: A Study of Microsoft's Early 2026 Rollout of Claude Code and GitHub Copilot CLI](https://mlsanity.com/paper/2607.01418/) (2026)
- [Human Capital, Not Model Benchmarks, Predicts Hybrid Intelligence in Forecasting](https://mlsanity.com/paper/2607.02467/) (2026)
- [MediaWiki Code2Code Search: Neural Retrieval for the Semantic Discovery of Open-Source Software Entities](https://mlsanity.com/paper/2607.26766/) (2026)
- [Generative AI Assisted Workflows in Architectural Conceptual Design: Performance, Creative Self-Efficacy, and Cognitive Load](https://mlsanity.com/paper/2601.10696/) (2026)
- [Human-AI Collaboration in Science at Scale: A Global Large-scale Randomized Field Experiment](https://mlsanity.com/paper/2605.24180/) (2026)
- [A robust association between LLM use and scientific productivity: Assessing stopping-time selection](https://mlsanity.com/paper/2607.28968/) (2026)
- [The Uneven Decline of Collective Knowledge Production: Evidence from Stack Overflow After Generative AI](https://mlsanity.com/paper/2609.36069/) (2026)
- [Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Prompting](https://mlsanity.com/paper/2610.02571/) (2026)

## Abstract (arXiv)

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.