Research · The Lag Index
The productivity lag as a single number. AI is deployed almost everywhere and visible almost nowhere in the output statistics — Solow's old paradox, returned. The Lag Index tracks how far the payoff has actually arrived, because the answer decides whether the build-out's financing outlasts its wait.
Deep in the trough. Inputs are nearly fully deployed; payoff has barely begun to register. The index is the share of the technology's productivity potential that has actually shown up in the aggregate statistics — and it is still in the single digits.
Lag Index = realized AI-attributable productivity as a share of full-diffusion potential. Inputs = enterprise adoption. Payoff = AI-attributable TFP (~0.07pp/yr) against a full-GPT-payoff benchmark (~1.5pp/yr, the late-1990s IT surge). Illustrative calibration; see sources.
The shape · adoption vs payoff
Plot the two curves on one scale — each as a share of full potential — and the lag is the white space between them. Adoption has raced to roughly 78%. Realized payoff has crawled to about 5%. Every prior general-purpose technology opened a gap like this; the question is only how long it stays open. Economist Torsten Slok frames the fork bluntly: the J-curve payoff could arrive in 2027 — or 2037.
Adoption and realized payoff as % of full-diffusion potential, 2018–2030. Solid = observed; dashed = the two scenarios from the trough. The shaded band is the lag. Adoption: enterprise surveys. Payoff: AI-attributable TFP vs the IT-surge benchmark. Projections are scenarios, not forecasts.
Calibration · the historical lags
A three-decade productivity pause while factories redesigned around the motor — then manufacturing TFP ran +5%/yr through the 1920s.
Solow's 1987 paradox — "computers everywhere except the statistics" — gave way to the 1995–2004 productivity surge.
Adoption faster than either predecessor; payoff not yet in the data. Whether the lag is IT-short or electricity-long is the whole question.
Research discipline · what would move the index
The index is built to move with the evidence. It climbs — and the bull case strengthens — if:
We track these on Capex Watch; when the official statistics move, the index moves with them.
Method & sources
The Lag Index is an illustrative calibration of published figures, not a proprietary econometric model; it expresses realized AI-attributable productivity as a share of full-diffusion potential. Figures are current as of mid-2026 and will move.
Adoption, "zero impact," and exec-usage figures: CEO survey via Fortune. AI-attributable TFP & the micro-macro gap: Kansas City Fed, BLS. The J-curve & "2027 or 2037": Torsten Slok / Apollo via industry reporting. Historical GPT lags (electricity, IT): general-purpose-technology literature (David; Brynjolfsson et al.). Cross-referenced with our own Capex Watch.
Not investment advice. Divergent Compute is a research institution; nothing here is a recommendation to buy or sell any security.