About This Calculator

Why this exists

This is an independent tool built to make the environmental cost of AI legible. The energy, water, and carbon footprint of using AI is widely discussed but poorly understood — often reduced to scary headlines or dismissive hand-waving. The honest answer sits in between, and it depends on what you actually do: which model, how long the answers are, whether you generate images, and which electricity grid powers the datacenter.

Rather than hand you a single dramatic number, this calculator shows a transparent estimate with an uncertainty range, built from public research you can inspect and adjust.

Our sources policy

Every figure here is cited and adjustable. The defaults come from peer-reviewed and widely cited public work — Epoch AI's inference estimates, Luccioni et al. (Power Hungry Processing, arXiv:2311.16863), Li et al. (Making AI Less Thirsty, arXiv:2304.03271), and EPA data for grid carbon intensity. The full reasoning lives on the methodology page.

We deliberately avoid relying on unverifiable vendor claims. Where a number is uncertain, we say so and present a range. In advanced mode you can override the water, PUE, and grid-carbon assumptions to match your own situation or a more recent source.

Limitations

These are estimates, not measurements. AI providers rarely publish per-query energy, so the figures are inferred and carry roughly a 10× range. A few specifics worth stating plainly:

• The carbon default uses the global-average grid (~440 g CO₂e/kWh), with a US preset (~348 g) selectable via the grid-region toggle; your real grid may be much cleaner or dirtier.
• The tool covers inference only — running your queries — not the one-time cost of training a model or manufacturing the hardware.
• The popular "Google search" comparison rests on a dated 2009 figure, included with that caveat because it is the most-requested comparison.

The goal is honest order-of-magnitude clarity, not false precision.

Suggest a correction

This methodology is meant to improve as better public data emerges. If you spot an error, have a newer source, or think an assumption is off, we genuinely want to hear it — corrections and pointers to published figures are welcome and help keep every number defensible. General suggestions and questions are welcome too.