What are we actually paying for when we buy AI? Not just intelligence, but different promises and different ways of sharing risk.
ChatGPT: Paying for Access
ChatGPT charges through subscription tiers. Within usage limits, OpenAI absorbs unpredictable computing costs, while users bear the responsibility of checking incorrect answers. A predictable monthly bill can still hide hours of human correction.
GitHub Copilot :Paying for a Person
GitHub Copilot charges per developer seat. Companies pay even when someone barely uses it, while GitHub covers included AI usage and may charge for more. If AI-generated code introduces a bug, developers still spend time finding and fixing it.
Intercom Fin: Paying for an Outcome
Fin charges for customer conversations considered resolved. This shifts more computing risk toward the provider when conversations remain unresolved. However, a customer who simply stops replying may still count as a resolution. The business still risks customer trust when AI gives incorrect advice.
Our Product?
Our AI product aims to improve cross-team collaboration for product managers.
Imagine leaving a meeting where design believes a feature will launch Friday, while engineering expects Monday. I want AI to reveal that misunderstanding, not pretend everyone agrees.
I would choose a flat team subscription with transparent usage limits. Per-seat pricing might discourage teams from inviting occasional collaborators, while outcome-based pricing would force us to quantify something as complicated as mutual understanding.
Pricing shapes behavior. If every additional voice costs money, teams may exclude the very people who need to be heard.
Ultimately, when AI makes a mistake, the real cost is not always computational. It can be someone’s time, trust, or a team’s shared understanding.
