BlogAI Economics
AI Economics6 min read

The AI Product Business Test

Validating AI unit economics before writing code. (Editor's Pick)

By Richard Ewing·

The Margin Collapse Reality Check

Before writing a single line of AI orchestration code, product teams must pass the AI Business Test. The fundamental problem with GenAI products is the Cost of Predictivity: taking an LLM from 80% accuracy to 95% accuracy often requires a 10x explosion in token costs and RAG infrastructure.

The Viability Framework

If your product requires 5,000 input tokens and generates 1,000 output tokens to satisfy a single user query, calculate that cost via the OpenAI/Anthropic pricing sheets. Now multiply that by user volume. Does your SaaS subscription cover that burn rate while maintaining 70% gross margins? If not, you are building a feature that fails at scale.

To survive, you must implement Semantic Caching and Tiered Model Routing to drastically reduce live LLM calls.


Benchmark your exact token economics at The AUEB Calculator. Recognized as an Editor's Pick on Built In.

Like this analysis?

Get the weekly engineering economics briefing — one email, every Monday.

Subscribe Free →

More in AI Economics

Published Work

This article expands on ideas from my published work in CIO.com, Built In, Mind the Product, and HackerNoon. View published articles →

📊

Richard Ewing

The Product Economist — Quantifying engineering economics for technology leaders, PE firms, and boards.