The token economics model is not scalable. AI economics needs to…

The token economics model is not scalable. AI economics needs to change.

Today, every time AI reasons, the meter runs.
That is acceptable when the problem is new, uncertain or exceptional.

But once the enterprise has already solved the problem, why should it keep paying to reason through the same logic again?

That is the idea behind my latest Technology Bytes:
The Enterprise Intelligence Compiler: From Probabilistic Reasoning to Deterministic Scale

Use AI where intelligence is actually needed: for the novel, unknown and exception paths.

Then take validated reasoning and turn it into executable business artifacts such as rules, workflows, policies, code, decision tables and controls.

That changes the economics:
Variable inference cost → Predictable execution cost
Probabilistic reasoning → Deterministic execution
Repeated inference → Reusable intelligence
Model behavior → Governed business logic
Scaling tokens → Scaling transactions

The goal is not to remove AI.
It is to stop using AI for problems the enterprise has already solved.

Reason until you know. Then execute what you know at scale.
If AI is going to scale across the enterprise, we need to move beyond an economics model where value is tied to how many tokens we consume.