Framework · Return on Tokens
ROT — is your AI actually paying?
Return on Tokens (ROT) is the unit economics of AI in production: value created per dollar of model/inference spend. Cost-side ROT measures hours saved and waste avoided against token budgets. Value-side ROT measures product velocity and revenue capture that have no floor of zero. If ROT > 0, ship; if ROT > 1, multiply deployment. Savings have a floor. Value creation doesn’t.
The flagship ROT essay — reworking “The Hidden Cost of AI Adoption” — lands here first. Mirrors on LinkedIn and elsewhere point to this page as canonical.
FAQ
- What is Return on Tokens (ROT)?
- The unit economics of AI in production: value created per dollar of model/inference spend — cost side (hours saved, waste avoided) and value side (product velocity, revenue capture) measured separately.
- How is ROT different from AI ROI?
- Most AI ROI framings stop at cost takeout. ROT splits the measurement: savings have a floor; value creation doesn’t. Reporting AI only as cost reduction understates the winning deployments and hides the losing ones.
- When should you ship?
- If ROT > 0, ship. If ROT > 1, multiply deployment. Measured in production — demos don’t count.