Measure the token input
Join input and output tokens to workflow, model, customer, unit cost, and the operating context in which they were consumed.
Token economics
How efficiently do your AI tokens turn into EBITDA?
Tokens are the measurable production input. Incremental EBITDA is the executive outcome. I help companies estimate the curve between them—connecting revenue lift, gross margin, labor savings, and cost-to-serve to the tokens that produced the change.
Get a defensible answer your CFO, board, and operating leaders can use to decide what to scale, fix, or stop.
Independent analysis. Decision-ready evidence. No vendor scorekeeping.
Executive measurement brief
Decision-ready
When token use increases, how much incremental EBITDA follows?
Token → EBITDA curve
Estimated, not assumed
Marginal yield
EBITDA per additional 1M tokens
Recommendation
Scale / optimize / stop
01Measure tokens
02Build the EBITDA bridge
03Estimate marginal EBITDA
04Allocate the next token
The measurement problem
AI usage becomes economically meaningful when it connects to EBITDA.
A usage dashboard tells you how many tokens were consumed. Token economics estimates the incremental gross profit and operating savings those tokens produced, whether marginal returns are flattening, and how much EBITDA would have happened anyway.
Build the EBITDA bridge
Translate revenue lift through gross margin, add operating savings, and subtract incremental AI operating costs to reach EBITDA contribution.
Estimate the response curve
Use experiments or econometric designs to estimate marginal EBITDA per million tokens, uncertainty, and diminishing returns.
Allocate the next token
Identify where another million tokens is likely to create the most EBITDA—and where usage should be rerouted, redesigned, or stopped.
Built for your situation
There is no honest universal token-economics calculator.
The EBITDA impact depends on your margins, workflow mix, adoption, counterfactual, model routing, labor design, and what decision must be made. The analysis should be built around those facts—not around generic assumptions.
Your EBITDA bridge
Revenue lift, gross margin, operating savings, quality effects, and AI costs enter differently for every business.
Your counterfactual
Token usage is not the treatment by itself. We define what would have happened without the AI workflow or with a different operating policy.
Your response curve
Average ROI can hide saturation. We estimate where marginal tokens still create EBITDA and where returns begin to flatten.
Your decision
Scaling, routing, vendor selection, workflow redesign, and budget allocation each require a different model and level of evidence.
The engagement
A decision model built with your operating reality.
- Diagnostic working session Clarify the executive decision, map the EBITDA mechanism, and assess what your current telemetry can support.
- Personalized measurement design Select the unit of analysis, counterfactual, econometric method, and uncertainty standard appropriate to your rollout.
- Decision-ready token economics Deliver the response curve, EBITDA bridge, limitations, and specific guidance on what to scale, reroute, redesign, or stop.
Confidential conversation
Bring the tokens and the operating outcome.
Tell me where your AI program stands and which EBITDA decision is blocked. I’ll review the measurement question and follow up directly.
- Measurement strategy grounded in your available data
- Methods selected for the decision—not for show
- Clear uncertainty, limitations, and next steps
Your inquiry is stored privately and used only to respond to you.
FAQ
What executives usually ask next
What is AI ROI?
AI ROI estimates net incremental EBITDA contribution relative to total AI cost. Token volume is the measurable production input; the EBITDA bridge is the financial output.
Why model EBITDA as a function of tokens?
It reveals the production curve: how incremental EBITDA changes as token consumption grows, where marginal returns begin to flatten, and whether the next block of usage is likely to pay back.
What should I count as AI spend?
Include model/API costs, software subscriptions, infrastructure, implementation costs, and ongoing labor where relevant.
Can we measure AI ROI without a randomized experiment?
Often, yes. The right quasi-experimental or time-series design depends on rollout timing, comparison groups, outcome frequency, and how adoption was assigned.
What if our data is incomplete?
The first step is a measurement audit: identify what can be estimated now, which assumptions matter most, and what instrumentation would make the next decision more credible.