"At that point, backpressure and load shedding are the only things that retain a system that can still operate. If you have ever been in a Starbucks overwhelmed by mobile orders, you know the feeling. The in-store experience breaks down. You no longer know how many orders are ahead of you. There is no clear line, no reliable wait estimate, and often no real cancellation path unless you escalate and make noise."
"That is what many AI-adjacent open source projects feel like right now. And increasingly, that is what a lot of internal company projects feel like in "AI-first" engineering teams, and that's not sustainable. You can't triage, you can't review, and many of the PRs cannot be merged after a certain point because they are too far out of date. And the creator might have lost the motivation to actually get it merged."
Writing code historically required more time than reviewing code, though queued reviews made reviews appear slower. Rapid increases in contribution volume can overwhelm review throughput, producing large backlogs and accumulating failure when input exceeds capacity. Open source projects and internal teams adopting AI-driven workflows can reach thousands of open pull requests, eroding the ability to triage, review, and merge changes. Delayed or out-of-date pull requests often become unmergeable and demotivating for creators. Without backpressure, load shedding, or other controls, the system degrades: reviewers lose situational awareness, waiting estimates vanish, and the development process becomes unsustainable.
Read at Armin Ronacher's Thoughts and Writings
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