The Meter Is Running: Why Public Sector AI Strategy Now Starts With Tokenomics

As governments move from AI pilots to enterprise deployment, the decisive question is shifting from which model to use to what each answer costs, whether that cost can be predicted a fiscal year in advance, and when it pays to own the capacity rather than rent it.

In September, the federal government changed how it pays for AI.

For the past year, agencies could get frontier AI for almost nothing. Under the General Services Administration’s OneGov program, ChatGPT Enterprise, Claude and Gemini for Government each cost a dollar or less. Those offers are giving way to commercial terms, and GSA’s new 27-month agreement with OpenAI is priced on a different principle: a 50% discount on token-based usage, no platform-access fee, no minimum order and no spend commitment. In GSA’s words, agencies “pay only for what they use.”

That is a good deal. It is also a different kind of deal. A flat fee is a budget line. A meter is a forecast. And because the new agreement is open to state, local and tribal governments, this is not only a federal story.

Public sector AI has just become a variable cost, priced in tokens, the word fragments AI models read and write. Most agencies do not yet have the instrumentation to predict it.

The trap: falling token prices, rising bills

AI tokens are getting cheaper, so budgets should be getting easier. Only the first half is true. At Dell Technologies World 2026, Jeff Clarke, Dell’s Vice Chairman and Chief Operating Officer, reported token costs down roughly 80% year over year and token use for reasoning alone up 320 times.

Unit prices fall. Consumption outruns them.

Uber can absorb the overrun. A government agency has no such cushion. It operates on an appropriation, and for a federal agency, spending beyond one is not a variance but a violation of law. In a state or county the same overrun becomes a supplemental request, a deferred project, or a service that stops in March.

In government, cost predictability is a condition of deployment.