A customer recently told me their team was spending six or seven times more on GPU inference than on cloud. Six to seven times. If that doesn’t reframe where FinOps needs to focus next, nothing will.
I’ve been sitting with these ideas for a while, and last week at FinOps X 2026 in San Diego, I got to dig into all of it live with John Furrier and Paul Nashawaty at theCUBE.
Here’s what I believe is true.
We Are All Senior Developers Now
AI is not taking jobs. It is multiplying people. Every developer on your team, regardless of years of experience, now has access to capabilities that used to require five years of seniority. A new college hire today can produce what a senior engineer produced three years ago. That is not a threat. That is an extraordinary gift.
But there is a subtlety here that organizations are getting wrong. Many companies decided early on: no new entry-level hires, because AI can do that work. Think about what that means in three to five years. If you never hire a junior financial analyst, where do your senior investment bankers come from? The career ladder doesn’t disappear just because the bottom rung gets easier to climb. You still have to climb it.
The right framing is not “AI replaces junior roles.” It is “AI makes junior roles perform like senior ones.” That is the compounding advantage, and it’s only possible if you give those people unlimited access to the tools that make them formidable.
Not All Tokens Are Equal
Here is where FinOps teams need to pay close attention, because the instinct to treat all AI spend the same way will cost you.
A token is not a token. LLMs are not just tiered by quality; they are specialized. The model you choose for a complex coding task is not the model you’d choose for a routine legal query. Picking the wrong model doesn’t just hurt your budget; it also hurts your output quality.
The practical reality: roughly 5% of a developer’s daily work genuinely requires a frontier model. The other 95% can be handled by simpler, cheaper, or open-source models with no meaningful difference in outcome. If you are running a flagship model at 100% utilization across your organization, you are paying frontier prices for commodity work.
The solution is to use AI to select AI. Rather than asking every developer to make model-routing decisions, the infrastructure layer should make those decisions automatically. The developer describes the outcome they want. The system selects the right model for each piece of the job. The developer only cares about the result. This is exactly how Cast AI approaches infrastructure optimization for Kubernetes: you define what you need, and autonomous agents handle placement, instance selection, and cost, invisibly, continuously, at scale across AWS, Google Cloud, Azure, Oracle, CoreWeave, Nebius, and Crusoe.
The same principle applies to the token layer. Right resource, right cost, right workload. Every time.
The Token Battery Problem
Imagine handing a developer a laptop with a one-hour battery that can only be charged once a day. That is exactly what it feels like to run out of tokens mid-task.
We see this pattern constantly. Budgets get set, token limits get imposed, and then a developer hits a wall at the worst possible moment, mid-reasoning, mid-debug, right before the solution. Many organizations have responded by imposing harder caps. That is the worst possible guardrail.
Cutting developers off is not governance. It is friction dressed up as control.
The right model is all-you-can-eat access for developers, with intelligent optimization running silently in the background. Open-source models where appropriate. Smart model routing. Autonomous cost management that the developer never sees or has to think about. The FinOps team’s job is to absorb that complexity and make it invisible, just as Cast AI’s autonomous agents absorb the complexity of node provisioning, spot instance interruptions, and workload rightsizing so that platform teams stop firefighting and start shipping.
“Have us, executives and FinOps teams, worry about how we supply you the token. Use them without any blocking. Use them as much as you want. We will ensure that you have as many as you need.”
That is the operating model enterprises need to build toward.
Autonomous Infrastructure Is Already Here
The mechanism that makes all-you-can-eat financially viable is autonomous automation, systems that self-select, self-heal, and self-optimize in continuous feedback loops.
At Cast AI, our agents buy millions of CPUs every day. They watch workloads, route jobs to the right instance type, handle spot interruptions with zero downtime, and continuously rebalance clusters for cost and performance, without a human in the loop. We have been doing this for years. The green/yellow/red dashboard era is over. Autonomous agents watching your infrastructure 24/7 keep it always green, resolving problems before they become line items on a report.
That same autonomous logic is what token optimization needs. An agent that watches your AI workloads, routes each task to the appropriate model, and grades the output against the defined standard. When it’s done, you get the result. You don’t need to know which model ran which step; the same way, our customers don’t need to know which instance type their pods landed on. Cast AI’s GPU optimization and OMNI Compute already extend this model into AI infrastructure: managing GPU utilization, enabling spot instances for stateful AI workloads via live migration, and expanding capacity across cloud providers when your primary region runs dry.
The infrastructure layer for the AI era is being built right now. The organizations that treat token economics the same way they learned to treat cloud economics, with automation, governance, and continuous optimization, will come out ahead.
FinOps Is About to Become the Most Important Function in the Building
At FinOps X 2026, J.R. Storment announced the Tokenomics Foundation. I am a founding premier board member, and I believe this is bigger than FinOps as we have understood it until now.
Think about the progression: DevOps brought financial accountability to software delivery. FinOps brought it to cloud infrastructure. Tokenomics brings it to AI consumption, which, based on what we are seeing with our customers, is already the dominant cost center for AI-native organizations.
This is not just an engineering problem or a procurement problem
. It is a CFO problem, a CHRO problem, a CIO and CISO problem. They all need to converge on someone who understands the economics of AI at the infrastructure level. That person is the FinOps practitioner.
The revolution is not coming. It is already here. We are lucky to be living it and building it in real time.
Laurent Gil is Co-founder and President of Cast AI, Governing Board Member of the FinOps Foundation and a Founding Premier Board member of the Tokenomics Foundation.
