What is AI Unit Economics Benchmark (AUEB)?
The AI Unit Economics Benchmark is Richard Ewing's framework for measuring the true cost and value of AI features.
The AI Unit Economics Benchmark is Richard Ewing's framework for measuring the true cost and value of AI features. It goes beyond simple inference costs to calculate the full economic picture: cost per useful output, hallucination cost, verification cost, and net value created.
The AUEB calculates: Cost of Predictivity (total cost per accurate AI output including failed attempts), Hallucination Cost (economic impact of incorrect outputs), Verification Overhead (human review time required), Net AI Value (value created minus total costs), and Break-Even Volume (queries needed for AI feature to be profitable).
The AUEB reveals whether AI features have positive or negative unit economics. A chatbot that costs $0.10 per query but only generates $0.05 in value has negative unit economics and will destroy margin as it scales.
The free AUEB tool at richardewing.io/tools/aueb provides automated AI unit economics analysis.
Why It Matters
Most AI features are launched without unit economics analysis. The AUEB prevents the "AI for AI's sake" trap by quantifying whether AI features create or destroy value.
Frequently Asked Questions
What is the AUEB?
The AI Unit Economics Benchmark measures the true cost and value of AI features, including inference costs, hallucination impact, verification overhead, and net value created.
What is Cost of Predictivity?
The total cost per accurate AI output, including: inference cost for all attempts (successful and failed), hallucination detection cost, human verification cost, and downstream error correction cost.
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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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