CONCEPT
The Quality Adjustment Problem
The measurement challenge of distinguishing price increases from quality improvements — a problem that strains existing methods for physical goods and breaks entirely when applied to AI-augmented cognitive output.
The Bureau of Labor Statistics employs several hundred people whose job is to decide whether a new car is better than last year's car, and if so, by how much. The question sounds simple. The answer is among the hardest problems in applied economics. When a manufacturer adds a backup camera, improves fuel efficiency, and raises the sticker price, the statistician must decompose the price increase into genuine inflation and quality improvement. Get the decomposition wrong in one direction, inflation is overstated. Wrong in the other, real output growth is understated. The entire edifice of real GDP rests on these quality adjustments, performed through hedonic pricing models. For cars the methodology works tolerably. For software it strains. For AI-augmented cognitive output it breaks entirely.
In The You On AI Field Guide
Coyle has identified quality adjustment as one of the chronic weaknesses of the national accounting system — a weakness that compounds silently because the errors are invisible in the headline figures.