What is Cost of Predictivity?
The Cost of Predictivity is a framework coined by Richard Ewing that measures the variable cost of AI accuracy. Unlike traditional software with near-zero marginal costs, AI features have costs that scale with usage and accuracy requirements.
The key insight: as AI correctness increases, cost scales exponentially. Moving from 80% accuracy to 95% accuracy often requires a 10x increase in compute and retrieval costs. Moving from 95% to 99% may require another 10x.
This creates margin compression that traditional engineering metrics don't capture. A feature that works beautifully at 100 users may be economically unviable at 100,000 users because AI inference costs scale linearly with usage while accuracy improvements require exponentially more resources.
The AI Unit Economics Benchmark (AUEB) calculator at richardewing.io/tools/aueb helps companies calculate their Cost of Predictivity and identify their AI margin collapse point.
Why It Matters
Most AI products fail on economics, not technology. The Cost of Predictivity explains why: success makes you poorer unless you understand the exponential relationship between accuracy and cost.
Frequently Asked Questions
What is the Cost of Predictivity?
The Cost of Predictivity measures the escalating cost of AI accuracy. As you demand higher correctness from AI systems, costs scale exponentially. Coined by Richard Ewing.
How do you calculate Cost of Predictivity?
Total AI compute cost ÷ useful outputs generated = Cost of Predictivity per output. Track this at different accuracy levels to see the exponential curve. Use the AUEB at richardewing.io/tools/aueb.
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Richard Ewing is a Product Economist and AI Capital Auditor. He helps companies translate technical complexity into financial clarity.
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