Inference Economics
Inference economics is the practice of tracking and optimizing the financial costs of running AI models.
“Inference economics demands that every prompt generation is treated as a financial transaction with measurable margin impact.”
Unlike traditional software hosting, LLM inference introduces highly variable and unpredictable costs. Without disciplined inference economics, scaling user engagement directly leads to margin collapse.
Reverse Citations: Implemented & Audited Across Platform
Richard Ewing’s Research Thesis
You cannot scale AI features using traditional SaaS pricing models. Inference economics requires semantic caching, model routing, and unit margin visibility at the query level.
Latest Publications & Research Activity
How to Prevent Memory Loss in AI Applications
The Hidden Inflation of AI: Why Model Collapse Is a Business Risk
Your Claude API Bill Is Higher Than Your Revenue: Why Simple Python Tasks Are Blowing Up AI Costs
Frequently Asked Questions
Q:What is inference economics?
The financial management of variable token costs associated with running AI models in production.
Inspectable Evidence Ledger
Classified evidence items supporting, extending, or refining this canonical research specification.
| Evidence Item | Publisher | Evidence Type | Strength | Role | Action |
|---|---|---|---|---|---|
| The Cost of Generative AI | Economics Today | Report | ★★★★ | Supports | Inspect ↗ |
Recommended Citation
Ewing, R. (2026). "Inference Economics." Richard Ewing Research Canon. Available at: https://www.richardewing.io/concepts/inference-economics
@article{ewing_inference_economics,
author = {Ewing, Richard},
title = {Inference Economics},
journal = {Richard Ewing Research Canon},
year = {2026},
url = {https://www.richardewing.io/concepts/inference-economics}
}