We Need an Emission Test for AI

We test cars for emissions before they're allowed on the road. We rate appliances for energy efficiency. We slap labels on buildings telling you how much power they consume per square meter. AI agents get none of this. No one asks how many tokens a system burned to answer a yes-or-no question. Invisible Waste Every token an LLM generates costs energy. Real electricity, real cooling, real hardware deprecation. A model that generates 2,000 tokens of preamble, caveats, and filler to deliver 40 tokens of actual information is producing waste. Physical, measurable, environmental waste. Nobody's measuring it. We're in the "leaded gasoline" era of AI. The technology works, people love it, and the externalities are completely unpriced. What Would This Look Like A standardized benchmark for efficiency , not accuracy. Given a set of tasks with known correct answers, how many tokens does the system consume to get there? Four metrics: 1. Token Efficiency Ratio (TER) TER = useful_output_tokens / to