What does this equation mean?

The formal relationship goes back to Theodore Wright’s 1936 observation that aircraft manufacturing labor hours fell as a power-law function of the number of airframes already built, and Way et al. build their entire forecasting method on a stochastic version of it, fitted to more than fifty technologies’ historical cost and production records [ 1 ] . Written in its cleanest form, cost c at cumulative production (experience) z relates to a reference cost $c_0$ at reference experience $z_0$ by: $c(z) = c_0 \left( \frac{z}{z_0} \right)^{-b}$. where the experience exponent b sets the learning rate LR = 1 - 2^{-b} , the fractional cost drop for every doubling of z . Way et al.'s working version of the same relationship is stated as a stochastic first-difference equation in log-cost against log-experience, with an estimated autocorrelation parameter of 0.19 held fixed across all fifty-plus technologies studied, chosen because fitting it separately for each technology’s comparatively short historical record degraded genuine out-of-sample forecasting accuracy [ 1 ] . The mathematics is unremarkable; what matters for this argument is what it implies about heritability and compounding. A technology with a high, stable learning rate converts every unit of deployment — every subsidy dollar, every early adopter, every demonstration project — into a durable, non-reversing reduction in future cost, the economic analogue of a trait that increases fitness in every generation it is expressed. A technology without that property, no matter how much capital or attention it receives, does not compound in the same way; its next generation starts from roughly the same cost position as the last one did.