What Is AI Tokenomics?
Ashish Nadkarni, group vice president and global domain lead for enterprise infrastructure at IDC, says tokenomics is ultimately about understanding the relationship between AI workloads and the resources required to complete them.
“Tokenomics is the cost of a token, and the economics surrounding how many tokens you need to get a task done,” Nadkarni says.
A token serves as a unit of work inside an AI system. Nadkarni compares tokens with other infrastructure metrics that enterprises already understand, such as storage IOPS (Input/Output Operations Per Second) or CPU cycles.
“Think of an AI token as a way to tie together all of the different resources to get an outcome accomplished,” he says.
That abstraction matters because enterprise AI workloads rarely consume infrastructure evenly. Different prompts, models and workflows can create dramatically different resource demands even when users appear to be performing similar tasks.
A simple AI request may consume relatively few tokens, while a complex, multistep workflow involving retrieval, summarization, analysis and orchestration can drive token use significantly higher.
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Why Agentic AI Is Exploding Token Budgets
The rise of agentic AI systems is accelerating token consumption. Unlike conventional prompt-response interactions, agentic AI workflows often operate autonomously across multiple stages, repeatedly invoking models, retrieving information, evaluating outputs and triggering additional tasks until a broader objective is completed.
“Once you fire off an agentic AI work stream, it’s not going to stop till it accomplishes the outcome,” Nadkarni says.
However, many enterprises still lack visibility into how efficiently those workflows operate internally.
“In the process, it might be inefficient or doing things that are extraneous,” he says. “Nobody has a way to look at the efficiency of that work stream.”
That inefficiency can quickly compound infrastructure costs: Repetitive reasoning loops, unnecessary retrieval operations and poorly tuned orchestration pipelines may all consume additional tokens without improving business outcomes.
Nadkarni cautions that while organizations may know AI systems are generating value, they often lack mature governance frameworks capable of tying infrastructure consumption directly to business metrics.
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How the AI Factory Model Is Changing Infrastructure Planning
Those economics are also reshaping enterprise infrastructure strategies through what Nadkarni describes as the emerging “AI factory” model.
Rather than treating AI as an isolated application layer, organizations are beginning to optimize entire infrastructure stacks around efficient token consumption and delivery.
“It means you set up a fully integrated system that is efficient or optimized for token use and token delivery,” Nadkarni says.
That optimization extends across compute, memory, storage and networking infrastructure.
“The whole stack is optimized for token consumption by internal resources,” he says. “There is no wastage; the costs are in check.”
Nadkarni compares the concept with a manufacturing assembly line where delays, inefficiencies and idle resources are minimized as much as possible.
“You want to build an infrastructure stack that is very efficient and optimized,” he says.
That shift may have major implications for enterprise data centers, GPU planning, storage architectures and workload orchestration as organizations increasingly prioritize inference efficiency rather than raw AI performance alone.
