The discussion about the energy consumption of artificial intelligence systems is important and, at the same time, full of figures circulating without a verifiable source. It is worth separating what can be said from what cannot.
What can be said with grounding
Training a large model consumes significant energy. It is a concentrated cost, paid once per model.
Use consumes less per request, but it multiplies. A single request is energetically cheap. Billions of requests are not. For a model used at scale, cumulative usage can exceed training.
Variation between models is large. A small, specialised model can consume orders of magnitude less than a large generalist one, on the same task.
Location matters. A data centre powered by low-emission sources has a very different footprint from one powered by fossil fuels, at identical consumption.
Energy is not the only resource. Cooling consumes water, and manufacturing equipment has its own footprint.
What cannot easily be claimed
Precise figures for the sector's total consumption, comparisons of the form \u201cone request equals X\u201d, or ten-year projections are, at best, estimates with wide uncertainty. Providers rarely publish detailed data, and methodologies differ so much that direct comparisons are often meaningless.
An impressive figure with no methodology attached is not an argument, in either direction.
What an organisation can actually do
Match the model to the task. Using the largest available model for a task a small one handles is direct waste — of money and of energy.
Avoid needless recomputation. Caching results for identical or very similar requests cuts consumption with no quality trade-off.
Keep the context to what is necessary. Sending a whole document when two paragraphs suffice multiplies the volume processed on every request.
Ask the supplier. Data centre locations and energy sources are legitimate procurement questions, and the answers can be compared between bidders.
Measure your own consumption. For locally run systems, consumption is measurable. For API-based ones, processed volume is a useful proxy.
The honest position
Consumption is real and worth counting, like any cost. It justifies neither rejecting the technology outright nor ignoring the issue. What it does justify is the habit of choosing a tool proportionate to the problem — a good principle anyway, regardless of the environment.