# On Autopilot? An Empirical Study of Human-AI Teaming and Review Practices in Open Source

arXiv: https://arxiv.org/abs/2601.13754 · HTML: https://mlsanity.com/paper/2601.13754/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2026.

## Built on by (most cited first, 5 of 5)

- [Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecycles](https://mlsanity.com/paper/2605.08017/) (2026)
- [Generative Models Erode Human Temporal Learning Through Market Selection](https://mlsanity.com/paper/2606.06572/) (2026)
- [Augmentation with Dilution: A Large-Scale Empirical Study of Human Contributor Ecosystems After AI Coding Agent Adoption](https://mlsanity.com/paper/2606.26289/) (2026)
- [3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse](https://mlsanity.com/paper/2607.07980/) (2026)
- [How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests](https://mlsanity.com/paper/2607.21832/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) increasingly automate software engineering tasks. While recent studies highlight the accelerated adoption of ``AI as a teammate'' in Open Source Software (OSS), developer interaction patterns remain under-explored. In this work, we investigated project-level guidelines and developers' interactions with AI-assisted pull requests (PRs) by expanding the AIDev dataset to include finer-grained contributor code ownership and a comparative baseline of human-created PRs. We found that over 67.5% of AI-co-authored PRs originate from contributors without prior code ownership. Despite this, the majority of repositories lack guidelines for AI-coding agent usage. Notably, we observed a distinct interaction pattern: AI-co-authored PRs are merged significantly faster with minimal feedback. In contrast to human-created PRs where non-owner developers receive the most feedback, AI-co-authored PRs from non-owners receive the least, with approximately 80% merged without any explicit review. Finally, we discuss implications for developers and researchers.