Image source: Pixabay.com/Placidplace

TL;DR

Unlike traditional SaaS, AI agents deliver uncertain value at a cost that keeps shifting, and that combination breaks the pricing logic SaaS was built on.

Every pricing model on the market today is a bet on a different definition of value, and each bet creates its own unit-economic problem.

Your pricing metric dictates what you have to monitor internally. Most teams build these two things separately.

A product that looks profitable in aggregate can lose money on a specific feature. We found one that had been losing money for months, and fixing it saved roughly $800K a year.

The better a customer support agent gets at knowing when to hand a case to a human, the less its vendor may earn from that conversation. It's one of the odder side effects of how AI agents are priced today, and it says a lot about how unsettled the market still is.…