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Foundations · AI line

The unit for an AI capability

5 min on the light depth

The frame

An AI capability can be priced on a seat, on usage, on an outcome or on an agent. Each is a value metric, the unit your price attaches to, and each asks something different of the customer. A seat asks them to count people. Usage asks them to forecast activity. An outcome asks both sides to agree what success is, who confirms it and who absorbs the gap. An agent asks what the agent replaces and whom it reports to. The labels the market debates are wrappers around that one decision.

This is the first AI decision you face. The request usually arrives with a unit chosen: price the agent against the salary it replaces, or fold it into the seat because the arithmetic came out small. Both skip what the customer is asked to count. Agent licensing anchors to a human, the person the agent works for or reports to, so the agent’s work stays inside a grant someone signed. Outcome pricing turns on who defines success and how a dispute is settled, agreements to reach before a price exists.

On the menu, how to choose an AI pricing model takes the label list apart, and AI monetization argues base or add-on has no universal answer. On the agent, pricing against the labor budget argues which budget pays is positioning while the unit stays undecided. The AI licensing model argues the grant must address the person, the agent and the model. On the outcome, what outcome-based pricing is argues a completed task is consumption relabeled. The piece on when it holds argues its conditions must hold at once, starting with the customer’s own count of value. Who defines success argues naming, confirming and absorbing are separate decisions with separate owners.

Which unit does your AI capability's price attach to, and what does that unit ask your customer to count, forecast or agree to before they sign?

Three things to say in the room this week

  • A seat asks the customer to count people; usage asks them to forecast activity. Which are we asking of ours?
  • If we price an agent, the license names the person it works for and reports to.
  • Before we price an outcome, we need to agree who names it, who confirms it and who absorbs the gap.

Core reading

Go deeper: the heavy depth

Read the core in order.

  • How to choose an AI pricing model: the label menu taken apart; what decides which unit holds.
  • AI monetization: base edition or add-on has no universal answer; monetization precedes price.
  • Pricing AI agents against the labor budget: which budget pays is positioning; the role’s cost moves with a market outside your control.
  • The AI licensing model: the grant addresses the person, the agent and the model; the agent is usually left open.
  • What outcome-based pricing is: risk shared in the customer’s realized result; per task is consumption relabeled.
  • Outcome-based pricing for AI software: the conditions that hold at once, the customer’s own count of value first.
  • Who defines success in outcome-based pricing: naming, confirming and absorbing have separate owners.

Then the shelf.

Seat. AI agents and seat-count repricing: anchor user descriptions to humans, then watch the agents. Agentforce pricing: constructs read against the three decisions in reverse. Outcome-based versus consumption-based AI pricing: credits and resolutions are different bets.

Usage. MCP server pricing: hosting is a floor; tool calls track behavior, which differs from value. Pricing against the DIY AI alternative: the buyer’s build estimate drags the reference price. When the agent is wrong: the meter’s position decides who pays for the run.

Outcome. Designing outcome-based pricing: a risk transfer needing upside; capping the bill is the costliest instinct. Output-based pricing: per delivered artifact, between input metering and outcome pricing in risk. Pricing per resolution: the charge moves from attempt to success; the resolution rate’s path has to be priced.

Agent. Agentic AI pricing strategy: employee, subscription and usage framings dodge the upstream unit decision. Service as software pricing: the deal prices against the role replaced, the renewal against a build estimate; the unit decides which you survive.

Research shelves: performance-based contracting and AI pricing, every paper described by provenance with a written ceiling.

Next on the AI track: Whose unit: your supplier’s meter and yours.

The shelf

Which depth did you take?