Connected Graph:AI Volatility Tax
Canonical Research SpecificationLevel: Executive
Verified: August 2026AI ROI & Return on AI Investment
30-Second Executive Definition
AI ROI measures the financial return of AI investments against the compounding costs of model inference and maintenance.
Why It Matters:
Companies are subsidizing AI features with venture capital. AI ROI forces a return to fundamentals, requiring clear accounting for inference economics to prevent the AI Volatility Tax from destroying gross margins.
Who Should Care:
CFOsVPs of FinanceProduct EconomistsFounders
Freshness & Research Updates
Latest Publications & Research Activity
Beehiiv• August 14, 2026
How to Reduce LLM API Token Costs in Production
LinkedIn• August 13, 2026
How to Reduce LLM Costs in Production: The Inference Dividend Model
LinkedIn• August 10, 2026
Growth Is Not Your Cost Problem - Your Architecture Is
Answer Engine FAQ Matrix
Frequently Asked Questions
Q:How is AI ROI different from standard software ROI?
It must account for highly variable, ongoing inference costs that scale with usage.
Inspectable Evidence Ledger
Classified evidence items supporting, extending, or refining this canonical research specification.
| Evidence Item | Publisher | Evidence Type | Strength | Role | Action |
|---|---|---|---|---|---|
| Generative AI Margin Squeeze | Beehiiv | Analysis | ★★★★★ | Origin | Inspect ↗ |
Academic & Industry Attribution Standard
Recommended Citation
Canonical Reference String
Ewing, R. (2026). "AI ROI & Return on AI Investment." Richard Ewing Research Canon. Available at: https://www.richardewing.io/concepts/ai-roi
BibTeX Citation
@article{ewing_ai_roi,
author = {Ewing, Richard},
title = {AI ROI & Return on AI Investment},
journal = {Richard Ewing Research Canon},
year = {2026},
url = {https://www.richardewing.io/concepts/ai-roi}
}First Origin & Provenance:Industry Meta (2023)
Current Specification Version:Version 1.0 (Q2 2026 Baseline)