The linked piece reports that Godot, the open source game engine, is tightening its contribution policy to reject AI-authored code and AI-generated human-facing text such as PR descriptions. The project’s stated reason is not that every machine-generated patch is automatically wrong. It is that maintainers need contributors who can explain design choices, respond to review, and eventually help maintain what they add. Godot also says AI may still be acceptable for menial assistance and that machine translation is fine if the original text was written by a person.
Most of the high-signal reaction agreed with the decision on blunt operational grounds. Reviewers described AI PRs as oversized, verbose, and cheap to generate, which breaks the old social contract around open source contributions. A large patch used to imply real effort and some investment in the project. Now it can mean almost nothing beyond a prompt and a desire for resume points, bounty money, or course credit. That changes how much explanation maintainers are willing to give and why they want an explicit rule they can point to when closing PRs quickly. Several comments sharpened the point further: the policy is mainly a governance tool for fast rejection and less a reliable attempt to detect every hidden use of AI. If someone uses AI carefully, reviews everything, writes a tight human explanation, and submits a small focused fix, they may be indistinguishable from any other competent contributor. The real target is the flood of low-accountability submissions.
Where the conversation landed was broader than Godot. People kept coming back to open source as a social system, not just a code ingestion pipeline. Review is partly about improving code, but it is also about training future maintainers, testing whether a contributor understands the codebase, and deciding who is worth investing scarce attention in. That is why many commenters rejected the idea that maintainers should judge code without caring about authorship. Others pushed back and said a blanket AI ban is a crude proxy for what projects actually want, which is small, testable, well-motivated changes with clear ownership. There was also a recurring economic angle: hiring-market pressure, university assignments, bug bounties, and GitHub-as-
CV are already producing contribution spam, and AI simply scales it. A smaller but vocal group argued that projects banning AI now may lose velocity later if models keep improving. That view drew skepticism from engineers who have used these tools heavily and said the near-term pattern is still the same: bursts of speed up front, then cleanup, regressions, and reviewer fatigue unless humans keep very tight control.