There is a pattern that repeats itself every time a new technology enters the enterprise.
Teams rush to use the tool, leadership celebrates the rollout, and success gets measured by activity. The metric is almost always volume-based because volume is easy to count. With cloud, it was compute hours. With agile, it was story points and tickets closed. With AI, it has been tokens consumed.
Tokenmaxxing, as it has come to be called, is the belief that more AI usage equals more AI value. The more tokens your teams burn, the more AI-forward your organisation is. Boards track it. Procurement uses it to justify spending. Engineering leaders report it upward as evidence of transformation.
It is lines-of-code with a bigger bill.
The Metric That Felt Like Progress
The token-as-metric era made sense in the beginning. Enterprises were trying to get thousands of employees to change how they worked. Adoption was the real problem, and usage data was the most available proxy for progress.
That phase served a purpose. It is over.
Most large organisations have now cleared the adoption hurdle. Engineers are using AI coding tools. Analysts are using AI for research. QA teams are deploying AI-generated test suites. The question has shifted from "are people using AI?" to "is the work AI produces actually reliable?"
That is a very different question, and token counts cannot answer it.
The Failure Mode Nobody Talks About
In software, the gap between activity and outcome can be invisible until it causes real damage.
An AI agent can generate thousands of tests that all pass, the coverage dashboard turns green, and the system is no more reliable than before. The tokens were consumed. The work looked complete. The software was still broken. Nobody found out until a customer did.
This is the failure mode that tokenmaxxing obscures. It rewards the agent for producing output, not for the output being correct. And the more autonomous AI systems become, the more expensive this failure mode gets.
A long-running agent that is confidently wrong costs the enterprise twice. First in the tokens it burns building something incorrect, and then in the downstream failures it creates or fails to catch. The cost of AI is no longer just the API bill. It is the cost of trusting the output when the output was wrong.
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The ROI Reckoning Has Arrived
CFOs and engineering leaders are now asking questions that FinOps dashboards were never designed to answer. Not "what did we spend on AI?" but "what did we get for it?"
FinOps tools are genuinely useful. They help enterprises understand AI cost, manage spend, and allocate budgets. But cost control does not tell you whether the output was correct. A well-managed token budget and a broken production workflow can coexist very easily.
The ROI question in enterprise AI has always been about outcomes. Reduced defect rates. Fewer production incidents. Faster release cycles with higher confidence. Compliance coverage that holds up under audit. These are measurable outcomes that require a different layer of infrastructure to track.
The Correctness Layer
What enterprises are beginning to build, and what the market is starting to demand, is a correctness layer. A system that does not just generate AI output but verifies it. That answers the question the token dashboard cannot: did the AI actually solve the task?
This shift is already visible in how the developer community evaluates AI systems. Benchmarks like HumanEval, SWE-bench, and APIEval-20 exist precisely because the field recognised that token fluency is not the same as task completion. The evaluation layer is now as important as the generation layer.
The same shift is coming to enterprise workflows. Teams will still use AI to generate code, write tests, review APIs, and automate processes. The difference is that serious organisations will also run a verification pass. Did the generated test catch a real bug? Did the API behave according to the spec? Did the agent's change break something downstream? These are pass-or-fail questions with verifiable answers.
The Next AI Winners
Every proxy metric eventually gets replaced by the thing it was trying to measure. Story points gave way to shipped products. Compute utilisation gave way to business outcomes. Tokens will give way to correctness.
The next phase of enterprise AI will be defined by organisations that built the infrastructure to verify what AI produces, not just generate it. That layer is where durable advantage will be built.
Tokenmaxxing was always a proxy. The end game is correctness.
The article has been authored by Abhishek Saikia, co-founder and chief executive officer, KushoAI.
Disclaimer: The views expressed in this article are solely those of the author and do not necessarily reflect the opinion of NDTV Profit or its affiliates. Readers are advised to conduct their own research or consult a qualified professional before making any investment or business decisions. NDTV Profit does not guarantee the accuracy, completeness, or reliability of the information presented in this article.
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