What this completely misses is the massive value of “non-code” AI work. Some of the highest-value interactions with an AI assistant involve debugging stack traces, analyzing fuzzy business requirements, or understanding edge cases. An engineer might spend an hour conversing with an LLM to pinpoint a root cause, ultimately resulting in a clean, three-line fix. That is brilliant, efficient engineering. Yet, under a tokenmaxxing regime, that engineer looks less productive on the manager’s dashboard than the one who just asked the agent to spit out 500 lines of spaghetti code.

Some of the very best uses of AI are in thoughtful learning, discovery, and design. Like the brainstorming, whiteboarding, and conversations that have always provided the substratum for creating software, the value of that kind of AI use is almost impossible to estimate or distill into a metric.

The economics of waste

When the measurement we use to understand the health of software engineering becomes so divorced from the actuality, the financial mechanics turn violently against the enterprise. From an economic viewpoint, tokens are an input cost, not an output value. Worse, output tokens can cost up to five times as much as input tokens across top-tier frontier models. When you incentivize code generation, you are pressing on an incredibly leveraged part of the IT budget.