CONCEPT
Innovation Accounting
Ries's measurement framework for the period before business viability — when progress means reduction of uncertainty rather than revenue — now requiring extension to address AI's asymmetric acceleration of building over learning.
Innovation accounting was conceived to solve a specific measurement problem: how does a startup demonstrate progress when traditional metrics (revenue, profit, market share) are not yet applicable? The framework proposed three phases — establish a baseline of current metrics, run experiments designed to move them toward the ideal, and make the pivot-or-persevere decision based on trajectory. The logic remains sound. Its application in the AI age requires fundamental extension, because AI has introduced metric velocity problems, presentation confounding, and new categories of vanity metrics the original framework did not need to address. Build velocity, deployment frequency, and architectural sophistication are now determined by the tool's capability rather than the team's judgment, and measure the amplifier rather than the signal passing through it.
In The You On AI Field Guide
When experiments can run at machine speed, the volume of data increases but so does the noise. Each experiment introduces variation, and the cumulative effect of ten simultaneous sources of variation can