The AI industry has become obsessed with tokens. How many tokens did we consume? Which model is cheaper? How much does a million tokens cost?
But tokens measure what AI consumes. They don't measure what AI accomplishes, and they certainly don't measure the value of what it accomplishes.
In this episode of Agentic AI — The Future of Intelligent Systems, Navveen Balani explores why token economics becomes increasingly incomplete as AI moves from generating responses to autonomous agents performing real work.
An agent can reason, retrieve context, call tools, delegate, retry, re-plan, and execute. Every step keeps the meter running, whether or not it contributes to the final outcome.
The next evolution in AI economics is therefore:
Tokens → Outcomes → Value
Tokens measure consumption.
Outcomes measure accomplishment.
Value measures impact.
From customer-service agents to coding and procurement agents, this episode explores why the cheapest model or lowest token count may not produce the most efficient system, and why enterprises need to start measuring cost per successful outcome, useful work per unit of compute, and ultimately value delivered.
Because organizations don't ultimately want more tokens, model calls, or agent executions.
They want work completed and value created.
