Inferonomics
AI Economics Advisory Lab
A practical framework and modeling lab to evaluate unit economics, margin resilience, and cost drivers of AI features before they ship.
Built independently as personal research. No confidential or employer data is used.
What it helps leaders answer
- What is our cost per effective request and what drives it (input/output/overhead)?
- What’s the break-even revenue per user and required cache rate to stay profitable?
- How do architecture choices (RAG depth, compression, token caps) impact margin?
- What are the fastest levers to restore margin when usage grows?
Executive Brief (1-page)
A concise summary of risk level, break-even targets, dominant cost drivers, and recommended actions— suitable for CTO/CFO alignment.
Unit Economics Modeling
Scenario-based modeling for MAU growth, requests/user, token budgets, caching, and pricing assumptions.
Optimization Levers
Practical levers such as retrieval depth, history compression, input/output caps, and caching strategy.