Tokenomics, in the enterprise AI context, is the discipline of governing how energy and capital are converted into AI tokens, how those tokens are consumed efficiently, and how that spend connects to business value. Three layers: production, where your GPU infrastructure manufactures tokens; consumption, where model routing, caching, and prompt architecture determine what those tokens actually cost; and value, where spend maps to outcomes. This is why AI infrastructure is now a FinOps problem: the token bill starts long before the model provider invoices you. It starts in your Kubernetes clusters, your GPU fleet, and your autoscaling decisions.

Here are four things from yesterday that every FinOps and infrastructure team needs to hear.

1. The invoice is not the cost. And I see this every day.

Pooja Kumar, VP CTO Transformation and FinOps at Prudential Financial, said it from the main stage, and I agree completely: “AI is just another workload” is the most dangerous lie a FinOps team can tell itself. We have customers spending six to seven times more on GPU inference than on cloud. That gap does not show up on the model invoice. It lives in retry storms, in agentic chains where one prompt triggers 20 model calls beneath it, in GPU nodes reserved just in case, and sitting idle when the job finishes early. These are infrastructure failures before they are token failures. You cannot govern what you cannot see, and right now, most teams are only seeing the tip.

2. A token is not the same as another token. This is the unlock.

Frederik Pohl, VP, Head of FinOps and Data Solutions at SAP, and his colleague Maida Nazifi, Data Scientist at SAP, showed 12 months of exponential token growth with cost per token falling and total spend still doubling. As J.R. mentioned on stage, Pinterest is already tracing costs from silicon through to model routing with cost and performance data attached at every architectural decision. As J.R. noted from Adobe’s experience on stage, model routing is powerful until it breaks your cache: routing to a cheaper model that invalidates a warm cache can make it more expensive than the one you replaced.. These are second-order effects that do not show up in standard tooling. None of this surprises me. LLMs are not just different in quality; they are different in specialization. Roughly 15% of a developer’s work genuinely requires a frontier model, based on what we see across Cast AI customers. The other 85% does not. The teams winning right now are the ones letting the infrastructure decide which model runs which task. You describe the outcome. The system routes, grades, and iterates. You only care that you got an A. At Cast AI, this is how we build, and it is how we help our customers build. The era of manually picking models is ending the same way manual node provisioning ended. It is better because it is finished.

3. FinOps just got its biggest mandate. ⁠We need to all show up for it.

J.R. Storment announced the Tokenomics Foundation yesterday morning: vendor-neutral, inside the Linux Foundation, running alongside the FinOps Foundation. Token costs have done something cloud optimization never quite managed: they have put FinOps practitioners in the middle of boardroom discussions.Mike Eisenstein, Managing Director at Accenture, said it clearly on stage: “this is our moment.” The question is whether your team shows up with the full stack answer or just the invoice analysis.

The full Day 1 keynote is below. Watch the room react in real time.

The infrastructure layer, the model selection layer, and the business outcome layer. All of it. The organizations building that governance foundation today will set the unit economics that define the next decade. Everyone else will be catching up.

4. How to Start Solving the AI Infrastructure Cost Problem

I think the answer is not more dashboards. It is autonomous automation that runs beneath everything your developers touch, invisibly, continuously, without them ever having to think about it.

I tell our customers the same thing I believe for our own team: developers need all-you-can-eat tokens. Cutting them off mid-task is not governance; it is friction. The job of FinOps and infrastructure teams is to make unlimited feel real while governing the economics underneath. That is only possible if the infrastructure layer is fully autonomous.

In practice that means four things. First, continuous GPU rightsizing so inference workloads land on exactly the compute they need, not what someone provisioned for peak six months ago. Second, spot instances for AI workloads, which most teams avoid because they assume interruptions mean dropped jobs. With live migration they do not. Third, autoscaling that handles bursty agentic traffic, because an agentic job that fans out into twenty parallel model calls needs infrastructure that scales in seconds and releases just as fast when it is done. Fourth, multi-cloud capacity routing: as hardware shortages persist through 2028, workloads need to move automatically to where capacity exists across AWS, Google Cloud, Azure, CoreWeave, Nebius, and beyond.

This is what Cast AI does for AI infrastructure on Kubernetes today. And we are going further. We are currently building a new product called Kimchi focused specifically on token spend optimization, giving teams the visibility and control to govern not just the infrastructure that manufactures tokens, but the token consumption layer itself. It is early, but what we are seeing in the data from our customers already tells us this is exactly where the problem needs to be solved next.

The token economy is not slowing down. The only question is whether your infrastructure governance is keeping up.

Laurent Gil is President of Cast AI, a Governing Board Member of the FinOps Foundation, and a Founding Premier Board Member of the Tokenomics Foundation.

Take-aways from FinOps X 2026 in San Diego.