# Pricing AI Outcomes

**Track:** Discovery & Validation at AI Speed — AI for Entrepreneurship — complete (29)
**Framework / surface:** venture strategy
**Level:** Intermediate
**Prerequisites:** Service First, Software Later

**In one line:** Seats, usage, or outcomes — what to charge for when the product is labor, not a license.

## Theory, aesthetics & inspiration

AI-native products increasingly sell finished work rather than access to tools, and pricing is migrating to match: per-seat licensing assumes value scales with human headcount — exactly the assumption agents break — while usage pricing tracks cost and outcome pricing tracks value. Intercom's Fin resolving support conversations for a posted price per resolution made the pattern concrete: the unit sold is the completed job. Outcome pricing demands an auditable definition of "done," which is why it pairs naturally with eval discipline, and it exposes the seller to the model's variance, which is why margins must be modeled per-outcome rather than per-license. The exponential is the pricing founder's ally twice over: falling inference cost widens the margin on every outcome sold, and rising capability grows the set of jobs deliverable at a promised quality — provided the price was anchored to customer value, not to a token cost that will look quaint in eighteen months.

**Founder question:** What is the customer’s definition of “done,” and would they pay per instance of it?