The exponential curve behind open source backlogs
armanckeser.com
April 14, 2026
9 min read
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45/100
Summary
Open source contributions can experience significant delays, with some pull requests (PRs) waiting over a year for merges despite receiving approvals. Factors contributing to these backlogs include queuing theory dynamics and the allocation of time within the review process.
Key Takeaways
- Open source projects like Jellyfin often experience significant backlogs, with contributors waiting over a year for pull requests (PRs) to be merged despite receiving approvals.
- A 2024 Tidelift survey revealed that 60% of open source maintainers have quit or considered quitting, highlighting a widespread issue in the community.
- Queuing theory indicates that as a maintainer's workload approaches 100% utilization, the wait time for PRs grows exponentially, leading to longer cycle times and increased backlogs.
- Jellyfin merges approximately 20 to 35 PRs per month, with features making up only 21% of merges, indicating a preference for bug fixes over new features.
Community Sentiment
Positives
- Automated code review using AI can significantly reduce the time spent on trivial mistakes, allowing maintainers to focus on more complex architectural issues, which is crucial for project efficiency.
- Leveraging AI for code reviews could enhance the quality of feedback if better prompting techniques are employed, potentially improving developer experience and project outcomes.
Concerns
- AI code reviews are often unreliable, with a correctness rate of only about 80%, leading to increased workload as maintainers must verify both the original code and AI-generated feedback.
- The current implementation of AI in code reviews does not save time for maintainers, as they end up reviewing both the pull request and the AI's comments, which can be counterproductive.
