What is AI Margin Collapse Point?

The AI Margin Collapse Point is the specific usage volume at which an AI feature's variable costs exceed the revenue it generates, causing the feature to destroy margin rather than create it. Coined by Richard Ewing as part of the Cost of Predictivity framework.

Traditional software has near-zero marginal costs — serving the 1,000th user costs roughly the same as serving the 10th. AI features break this model: every query costs compute, and costs scale linearly (or worse) with usage.

The AI Margin Collapse Point = Revenue per AI query ÷ Cost per useful AI output. When cost exceeds revenue, you've passed the collapse point.

Many AI features that work beautifully in prototype (low volume, accuracy requirements are lower) become economically devastating in production (high volume, users demand high accuracy, support costs from hallucinations). The collapse point often surprises organizations because testing at 100 users shows positive economics, but production at 100,000 users reveals the exponential cost curve.

The AUEB calculator at richardewing.io/tools/aueb helps companies identify their specific margin collapse point before it hits the P&L.