As enterprises rush to deploy generative AI across their organisations, IT services firms are beginning to warn of a new challenge: token maxxing.
The term refers to the growing tendency among companies and employees to measure AI adoption through token consumption — the units used whenever large language models process information — rather than through actual business outcomes, according to a report by the Times of India.
Tokens have emerged as the basic currency of the AI economy. But after an initial wave of experimentation, technology companies are increasingly cautioning that unchecked token usage could create a fresh cost problem, much like cloud spending did for enterprises over the past decade.
"Tokens are an input to delivery, not a measure of value," Arumugam Kumaradassan, vice-president and head of AI industrialisation and enterprise IT automation at Cognizant, told TOI.
"When token consumption is treated as the primary metric, costs scale linearly with demand without a corresponding return in business outcomes," he said.
The phrase "token maxxing" has gained popularity in Silicon Valley, where the "maxxing" suffix — borrowed from internet and gaming culture — refers to aggressively optimising a particular metric. In the AI world, it increasingly describes a mindset where higher AI usage is automatically equated with higher productivity.
According to the report, IT firms are now trying to ensure that AI spending remains tied to measurable outcomes rather than raw consumption.
Cognizant has introduced systems that track token usage alongside business workflows and results, allowing customers to better understand whether AI spending is generating value.
The issue is becoming more relevant as enterprises move from AI pilots to production deployments involving thousands of employees and autonomous AI agents.
At Happiest Minds, co-chairman Joseph Anantharaju told TOI that the company is building token metering and optimisation capabilities as customers scale agentic AI projects.
"I think that's going to be very important — the ability to meter it," he said.
The company is also exploring outcome-based commercial models that combine software, AI agents, platforms and token consumption into a single framework tied to business goals.
The focus on efficiency is not limited to IT services firms.
Salesforce chief digital evangelist Vala Afshar said organisations are increasingly tracking not only how many tokens are being consumed, but also how many tasks are actually being automated as a result.
"It's wasteful to just spend tokens unless you're creating value at the speed of need," Afshar said, according to the report.
Technology providers are also becoming cautious about linking pricing directly to token consumption. Mphasis chief executive Nitin Rakesh told TOI that customers are increasingly bearing variable AI costs as usage scales, making outcome-based pricing models more attractive.
"What we are pricing is the economic outcome," Rakesh said. "There is a base price that you will pay me, and the rest will be linked to the outcome I can drive."
The debate highlights a broader shift underway in enterprise AI. As companies move beyond experimentation, the conversation is increasingly changing from how much AI is being used to whether that usage is generating measurable returns.
