mlsanityData to 14 Oct 2026

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

arXiv 2506.08945 ↗First posted 10 Jun 2025cs.CY cs.SE physics.soc-phcited by 18

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 15.

18 papers.

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  1. 2025Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projectscited by 24
  2. 2026The Impact of AI-Generated Text on the Internetcited by 6
  3. 2025Vibe Coding in Product Teams: Reconfiguring AI-Assisted Workflows, Prototyping, and Collaborationcited by 4
  4. 2026Chinese Language Is Not More Efficient Than English in Vibe Coding: A Preliminary Study on Token Cost and Problem-Solving Ratecited by 2
  5. 2026GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testingcited by 2
  6. 2026Minimal Prompt Perturbations Lead to Code Vulnerabilities: Prompt Fragility and Hidden-State Signals in Coding LLMscited by 2
  7. 2025Breaking Memorization Barriers in LLM Code Fine-Tuning via Information Bottleneck for Improved Generalizationcited by 1
  8. 2026Extracting Recurring Vulnerabilities from Black-Box LLM-Generated Softwarecited by 1
  9. 2026LLMs Generate Kitschcited by 1
  10. 2026Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoringcited by 1
  11. 2026Adoption and Impact of Command-Line AI Coding Agents: A Study of Microsoft's Early 2026 Rollout of Claude Code and GitHub Copilot CLIcited by 1
  12. 2026Human Capital, Not Model Benchmarks, Predicts Hybrid Intelligence in Forecastingcited by 1
6 more
  1. 2026MediaWiki Code2Code Search: Neural Retrieval for the Semantic Discovery of Open-Source Software Entitiescited by 1
  2. 2026Generative AI Assisted Workflows in Architectural Conceptual Design: Performance, Creative Self-Efficacy, and Cognitive Loadcited by 0
  3. 2026Human-AI Collaboration in Science at Scale: A Global Large-scale Randomized Field Experimentcited by 0
  4. 2026A robust association between LLM use and scientific productivity: Assessing stopping-time selectioncited by 0
  5. 2026The Uneven Decline of Collective Knowledge Production: Evidence from Stack Overflow After Generative AIcited by 0
  6. 2026Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Promptingcited by 0