AI economics done right puts Finance in the cockpit — where cost is controlled before it’s incurred
AI does not behave like SaaS. It behaves like a utility — cost is incurred with every use.
“AI economics done right” means aligning measurement, control, and pricing around the actual work being performed.
- FLOP-Based Metering (FBM) measures the work.
- Normalized Compute Units (NCU) standardize the unit.
- G-PEP enforces control before execution.
- Governance gives control to the customer.
- BAP packages it into predictable pricing.
The shift
Today: AI executes first, cost is discovered later
This system: cost is defined, approved, and bounded before execution occurs
If you cannot bound cost before execution, you do not have a pricing model — you have financial exposure.
| Today: Tokens, uncontrolled, after-the-fact | Proposed: Pre-execution, bounded, measurable |
|---|---|
|
Execution model Measure after execution Tokens approximate output, not work |
Execution model Estimate before execution Compute defines expected work |
|
Agent behavior Retrieval, retries, tool calls often invisible Work expands without corresponding tokens |
Agent behavior Bounded execution Depth, branching, and tools governed |
|
Control point No control before execution Adjustments happen after spend occurs |
Control point Control before execution Approve, cap, or deny before cost is incurred |
| Impact on Billing, Governance, and Finance | |
|
Billing Based on token output Same tokens ≠ same work Revenue leakage in agent workflows |
Billing Based on compute work Aligns price with actual cost Captures full agent activity |
|
Governance Reactive controls (limits, throttles) No pre-execution enforcement |
Governance Pre-execution controls Budgets, caps, approvals enforced upfront |
|
Finance Cost discovered after execution Forecasting unreliable Margin pressure from hidden compute |
Finance Cost known before execution Predictable spend Enables margin control and planning |