The need for industry standards on tokens

The clearest sign this is becoming a structural issue arrived this week when the Linux Foundation announced its intent to launch the Tokenomics Foundation — an initiative focused on establishing open industry standards, benchmarks and best practices for the economics of AI infrastructure, working in close partnership with the FinOps Foundation. 

Research from Goldman Sachs, cited in the announcement, projects global token usage will multiply 24 times between 2026 and 2030, reaching 120 quadrillion tokens per month, with the inference market expanding from roughly $106 billion in 2025 to $255 billion by 2030. Initial supporters already include Accenture, Booking.com, IBM, KPMG, Oracle, Salesforce, SAP and ServiceNow.

Industries don't create standards bodies for temporary problems; they create them when a problem is becoming structural and when no single vendor can be trusted to define the standard.

Beyond cloud-hosted API consumption

There’s a longer arc here that deserves attention. Several organizations are beginning to explore a trajectory beyond cloud-hosted API consumption: running capable models locally, on-device or on private infrastructure. The technical constraints remain real — throughput, model quality, operational complexity — but the gap is closing. Smaller, high-quality open-weight models are increasingly competitive on the task categories that consume the most enterprise token spend. The economics of self-hosted inference are becoming viable precisely because the economics of hosted inference are becoming more and more painful.

Teams experimenting with local deployment today aren’t being scrappy; they’re building the architectural judgment about which workloads belong on private infrastructure versus which require frontier capability — judgment that cannot be purchased when you need it and has to be developed in advance.

What's worth noting is that the token crisis is not the origin of this pressure — it is a downstream symptom of it. The physical infrastructure supporting AI scaled faster than the energy systems built to sustain it. Data center energy consumption is approaching 1,050 TWh in 2026, which would make data centers the fifth largest energy consumer in the world if counted as a country. Ireland's data centers are projected to consume 32 percent of the country's total electricity supply, a figure that has driven regulatory moratoriums on new grid connections.

The pattern isn't limited to one jurisdiction. Across multiple markets, projects secured years ago are stalled waiting for grid connections that don't yet exist. The token costs enterprises are managing today are, in part, the price signal of that physical scarcity being passed downstream. And this is precisely why the response has to be architectural rather than financial. The organizations responding well are re-architecting their entire delivery lifecycle. Upstream phases are shifting toward clearer planning, stronger specifications and deliberate context engineering; downstream phases toward containment boundaries and runtime oversight.

When the constraint is physical and compounding, you cannot optimize your way out of it at the billing layer. You have to move the decisions that generate the cost upstream, to where they can actually be governed.