the method of estimating hardware vs software share seems biased in the direction of exaggerating hardware share. (because training compute and software efficiency both tend to increase over time, so doing a univariate regression on training compute will include some of the effect from software improvements. so subtracting off that coefficient from the total will underestimate the effect of software improvements / “algorithmic progress”.)
From the paper:
Comparing these two estimates allows us to decompose the total gain into hardware and software components. By subtracting the compute effect (0.048) from the total effect (0.083), we isolate a residual of 0.035. This residual represents algorithmic progress, an economic catch-all for improvements in model architecture, software optimization, and user learning—effectively the Solow residual of AI production. In percentage terms, this decomposition suggests that compute scaling drives approximately 56% of the total reduction in time, while algorithmic advancements account for the remaining 44%.
the method of estimating hardware vs software share seems biased in the direction of exaggerating hardware share. (because training compute and software efficiency both tend to increase over time, so doing a univariate regression on training compute will include some of the effect from software improvements. so subtracting off that coefficient from the total will underestimate the effect of software improvements / “algorithmic progress”.)
From the paper: