What Is AI Tokenomics?
Ashish Nadkarni, group vice president and global domain lead for enterprise infrastructure at IDC, says tokenomics is about understanding the relationship between what you ask AI to do and what it costs.
"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 is the basic unit of work inside an AI system — a small chunk of text it processes. Every prompt and response consumes tokens; the more complex the task, the more tokens it uses.
"Think of an AI token as a way to tie together all of the different resources to get an outcome accomplished," Nadkarni says.
The same tool can cost dramatically different amounts depending on how you use it. A simple question uses very few tokens; a multistep workflow — researching a topic, drafting a response, and scheduling a follow-up — can use far more.
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Why Agentic AI Is Driving Up Costs
The shift toward "agentic AI" — tools that work autonomously toward a goal rather than responding to a single prompt — is where businesses most often see token costs spike.
"Once you fire off an agentic AI work stream, it's not going to stop till it accomplishes the outcome," Nadkarni says.
These tools can automate tasks effectively, but they can also run inefficiently without any visibility into what's happening.
"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."
Redundant steps, unnecessary lookups and poorly configured workflows drive up token use without better results. Companies using AI agents report 55% higher operational efficiency, but only when those systems are properly set up and managed.
