Tokenomics Brings FinOps Discipline to AI Cost Management
AI Cost Management Needs a Shared Language
AI cost management is becoming a board-level concern as organizations move from experiments into production use cases. In this Techstrong TV interview, Mike Vizard talks with Amit Kinha, Field CTO at DoiT, about the rise of tokenomics and the work of the Tokenomics Foundation.
Kinha explains that tokenomics is not only about lowering the price of tokens. It is about understanding the full economics of AI. That includes model usage, GPU cycles, infrastructure, application design, business value and the outcomes that AI systems produce.
Tokenomics Extends the FinOps Model
FinOps gave cloud teams a common way to discuss visibility, governance and optimization. AI introduces a faster and more complex version of that problem. Teams now need to understand what a specific AI task costs and whether that cost is justified by the value created.
That shift makes AI cost management more complicated than traditional cloud cost tracking. A single task can involve input tokens, reasoning tokens, output tokens, database calls, vector searches and agentic workflows. Each step can add cost. Each step also needs to be tied to business impact.
Value Matters More Than Token Price
The conversation highlights why price per task may be more useful than token price alone. A model that appears cheap can become expensive if it needs more steps to finish the work. A more expensive model may deliver better results if it completes the task faster or with fewer failures.
Kinha also explains why AI adoption depends on proving value. Engineering teams can often show faster delivery with coding tools. Other teams, such as marketing, sales, research and healthcare, may need different measures. The goal is to connect AI spending to real outcomes.
Context Engineering Becomes Critical
The discussion also turns to context engineering. AI systems need the right information to produce useful work. Too little context leads to weak answers. Too much context can create noise, risk and unnecessary cost.
For technology leaders, the takeaway is practical. AI cost management requires more than usage reports. Organizations need standards, measurement practices and governance models that show what AI costs, where that spend goes and what the business receives in return.
