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AI economics

AI Feature Attribution

How much of your ARR is actually driven by your AI features? Net of infra cost, with cohort gross margin.

Your numbers

Start from an example

$
Share of users on AI features
AI cohort ARPU uplift

How much more AI-feature users pay per year than other users.

$
AI cohort churn difference (points per year)

Annual churn of AI users minus other users. Negative means AI users churn less.

Gross margin before AI cost

Margin on revenue before AI infra. 80% is typical for SaaS.

Result

Status: in range.ARR attributable to AI
$139,535
Status: in range.Net after infra
$43,535
AI cohort gross margin
66.2%
ARR kept by lower churn
$13,953

1,500 AI-feature users pay $465.12 a year against a base ARPU of $372.09 for the other 3,500. Annual AI infra cost: $96,000.

ARR attribution split

Gross AI-attributable ARR vs net (after infra cost).

AI-attributable ARR (gross)
$139,535
AI-attributable ARR (net)
$43,535
Annual AI infra cost
$96,000

General business information, not legal, tax or financial advice. The result comes only from your inputs and the published method. How we check our numbers.

How to use it

  1. Enter your total ARR, the fraction of users who use the AI features, the ARPU uplift those users show, your monthly AI infra cost, the churn difference for AI users, and your total user count. The tool separates your user base into AI and non-AI cohorts and back-solves the base ARPU, so it isolates how much of your revenue the AI features actually earn rather than letting AI take credit for revenue it did not move.
  2. Read the ARR attributable to AI (the ARPU premium times the AI users), the net figure after annual AI infra cost, the AI cohort gross margin, and the ARR kept by lower churn; the line under the tiles shows the AI and non-AI user counts, both ARPUs and the annual infra cost. The net AI-attributable ARR is the honest number, because it credits AI only for the incremental revenue and then subtracts what the AI infrastructure costs to deliver it.
  3. Use the net figure to answer whether the AI features pay for themselves. It is common for an AI feature to show an impressive ARPU uplift that mostly evaporates once the infra cost is netted out, and this tool surfaces that gap directly instead of letting a gross uplift number justify the spend on its own.
  4. Read the ARR kept by lower churn as a second source of value, reported separately so it is not double-counted. If AI users churn meaningfully less, the retained revenue can rival the direct ARPU uplift, which reframes the AI feature from a revenue add-on into a retention investment. Test a conservative uplift and a higher infra cost together to see how robust the attribution is before you defend the feature's budget.
  5. Re-run as AI adoption spreads through your base, as infra costs change, or as you measure real cohort churn rather than estimate it. Pair this with the agent cost per validated customer tool for the acquisition-side economics, so you see both what AI features cost to run and what they earn across the full customer lifecycle.
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Use it from code

The same calculation as a JavaScript module you can import. It runs where you import it, with no request, key or rate limit.

import { compute } from "https://aibizhub.io/engines/ai-feature-attribution.js";

Input and output contract, TypeScript types and the guide for agents.