The AI industry loves token inflation. Your company shouldn’t

The AI industry has a quiet addiction problem: It is addicted to tokens.

Every new generation of agentic AI seems to assume that the answer to complexity is to throw more context at the model, keep longer histories, spawn more calls, loop over more tools, and let the token meter run wild.

The rise of agentic systems, and now projects like OpenClaw, makes that temptation even stronger. Once you give models more autonomy, they do not just consume tokens to answer questions. They consume them to plan, reflect, retry, summarize, call tools, inspect outputs, and keep themselves on track. OpenClaw itself describes the product as an “agent-native” gateway with sessions, memory, tool use, and multi-agent routing across messaging platforms—which tells you exactly where this is going: more autonomy, more orchestration and, unless someone intervenes, a lot more token burn.

That trajectory delights almost everyone selling the infrastructure. If billing is based on tokens, more token consumption looks like growth. If you sell the compute behind those tokens, it looks even better. Google said in its October 2025 earnings call that it was processing more than 1.3 quadrillion monthly tokens across its surfaces, or more than 20 times the volume of a year earlier. Nvidia, for its part, has been leaning hard into the economics of inference and agentic AI, highlighting both the demand surge and the opportunity to sell ever more infrastructure into it.

But companies buying AI should look at this very differently. From the customer’s perspective, explosive token growth is not necessarily a sign of intelligence. In many cases, it is a sign of inefficiency.

More tokens are not the same thing as more intelligence

The current industry narrative often treats token consumption as if it were a proxy for progress. Bigger context windows, more reasoning traces, more agent loops, more memory, more retrieval, more interactions. It all sounds impressive.

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