The next economic question
Demand no longer waits for a person
Part I examined a familiar corporate response to a visible meter: organizations optimize the unit, then shift attention toward what the technology produces. AI adds a complication. Software itself can now consume the meter.
A human request pauses for judgment between cycles. An automated workflow can call a model, invoke a tool, query a system and trigger the next action without waiting for another person. Budgets, rate limits and policy still impose boundaries; human attention no longer paces every unit of demand.
One request ends. One event can keep producing activity
Human attention is no longer the only governor of demand
Demand forecasting therefore cannot stop at users or prompts. It must account for interactions per business event, event frequency and the likelihood that one interaction creates another.
The rebound question
A cheaper unit can produce a larger bill
Once software can create demand, lower unit cost can expand aggregate consumption by making more workflows, greater frequency and deeper call chains economical. A Jevons-style rebound is a useful lens, not a prediction that every efficiency gain will be consumed. The narrower point is sufficient: a unit-cost forecast cannot substitute for a demand forecast.
A cheaper unit can make a much larger field of activity economical
Economic mechanism
Efficiency can expand the addressable workloadCurrent evidence of scale
Unit efficiency and aggregate economics can move in opposite directions
Google’s platform-wide figures establish magnitude, not causation. AT&T offers a separate enterprise signal: an average of 45 billion tokens per day in July 2026. The measures are not comparable, but they show token consumption at a scale where aggregate demand becomes a management issue in its own right.
For finance, a lower rate per token may coexist with higher total spend and still represent successful adoption. The question is whether the additional activity creates sufficient value after infrastructure, oversight and exception handling. Favorable unit economics and unfavorable aggregate economics can occupy the same dashboard.
The SMS precedent
Cheap automation changes what management has to manage
Text messaging offers a useful precedent. As message economics improved, software generated a larger share of traffic: authentication codes, order updates, appointment reminders and other application-to-person messages. Price per message became less revealing than who could send, what recipients had agreed to receive and which traffic should be delivered or stopped.
Governance emerged across industry practice and law. Mobile operators and messaging providers developed the 10DLC registration ecosystem to identify brands and declared campaigns; CTIA practices emphasize consent and consumer choice; FCC rules separately govern obligations such as honoring revocation. The architecture is specific to messaging. The progression from cheap capacity to control is the relevant precedent.
When automated volume scales, price becomes only the first management question
AI control surface
Conceptual relationship- Establish authorityIdentity · Permission
- Bound the actionScope · Traceability
- Contain failureStopping power
The meter remains in operations. The constraint moves into the operating model
AI will not reproduce the same architecture. The relevant lesson is economic: once software can initiate activity, control becomes part of the cost of scaling it. Orchestration, observability, evaluation and exception handling belong in the model alongside inference.