Nordhaus argues the learning-curve coefficient is statistically unidentified and biased upward — a zero-learning process still fits a coefficient of 0.2

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In "The Perils of the Learning Model for Modeling Endogenous Technological Change" (NBER w14638, January 2009), William D. Nordhaus argues that the coefficient at the heart of Wright's law / the experience curve cannot, in the standard specification, be separated from ordinary exogenous technological progress. Because cumulative production and time both trend upward together, a regression attributing cost decline to learning-by-doing absorbs unrelated progress into the learning term: "the estimated learning coefficient will generally be biased upwards." His numerical demonstration is the sharp edge — in a constructed case with zero true learning, the fitted parameter still comes out positive: "the empirical learning coefficient is 0.2 even though the actual learning coefficient is zero."

The empirical instability is of the same order. Across 34 industries, "only 4 have estimated empirical learning coefficients in the plausible range between 0 and 0.5," and the correlation between two reasonable alternative specifications is 0.009 — effectively no agreement about which technologies learn fast. The forecasting consequence Nordhaus draws is that model-selected "high-learning" technologies (he names solar and wind) can have their future costs "underestimated by a factor of two."

This complicates the forecasting confidence carried by Sahal's identity and the monotonic-decline mechanism in claim-cheaper-extraction-disruptions-fall-monotonically-not-hold-then-collapse: the shape of a past cost curve can be fit cleanly whether or not endogenous learning was actually driving it, so a fitted curve is weak evidence that a collapse will continue. Nordhaus's critique is one economist's argued position within his own model, not settled field consensus; it is recorded here as his claim, verbatim from a Tier 1 primary, rather than as a proven fact about the world.