AI Investments Should Focus On Value Creation Over Token Utilization

Dr. Chih-Han Yu is the CEO and co-founder of Appier, an AI-native agentic-AI-as-a-service (AaaS) company.

​This spring, a term briefly took hold in Silicon Valley: tokenmaxxing. The idea was simple. Many were trying to push token consumption as high as possible and treat usage volume as a proxy for AI adoption. Watching those conversations unfold, one question kept coming to mind for me: Does using more tokens mean producing more value?

Think of an AI agent as an employee and tokens as its salary. A higher salary doesn’t guarantee better work, and token volume alone tells you nothing about the value AI has created. At my company, we care less about how many tokens a task used and more about what it accomplished.

Token spend surged rapidly at some tech companies this year. At least one reportedly burned through a year’s AI budget in four months, as cited by Fortune. In that same article, Fortune noted that the tokenmaxxing craze is already fading.

None of this means companies should use less AI. It means token volume can no longer stand in for productivity. The bigger bill isn’t really the problem. The real question is whether token consumption has drifted from the task’s objective, its output quality and the business outcome it was meant to serve. What a company measures shapes what its people optimize for, and that shapes its AI culture. Reward using more tokens, and you’ll get more tokens used, whether or not any of them mattered.

My company has always been an ROI-driven business. We want every dollar our customers spend to generate a return, and we hold internal investment to the same standard. Calculating AI’s ROI is genuinely difficult, though, and it’s something we’re still refining.

How Do You Actually Measure The Return?

Start by separating what the investment is meant to achieve. Is this deployment meant to cut costs or to grow revenue? The two need very different approaches.