Impacts
AI uses electricity and water, contributes to carbon emissions and depends on material infrastructure. The figures below remain attached to the systems and boundaries they describe.
Energy
Electricity demand from data centres is large and growing. AI is a major driver, but the best macro figures do not isolate it from other digital services.
Both figures describe global data-centre electricity, not AI alone. The first is an estimate and the second is the IEA base-case projection. Per-query estimates also vary greatly with model, task and assumptions.
Sources: IEA, Jegham and co-authors
Water
Water can mean direct cooling, electricity-generation water, semiconductor manufacturing or a full lifecycle result. Location also changes the significance of every litre.
The raw values differ by about 173 times. This is not a provider ranking. They use different functional units and boundaries, so the distance between them partly reflects what each source counts.
Sources: Google, Mistral AI
Carbon emissions
Carbon results depend on electricity use, grid conditions, hardware and accounting choices. A precise figure can still describe only a narrow part of the system.
The raw values differ by 38 times. The prompts, models, lifecycle boundaries and electricity accounting are not aligned. Dividing the two values does not establish that one provider is 38 times more efficient.
Sources: Google, Mistral AI
Materials
AI hardware depends on mining, refining, semiconductor manufacturing, construction and end-of-life systems. These impacts are often outside operational measurements.
Mistral reports 0.16 mg Sb eq of abiotic resource depletion for one Le Chat response. This is a lifecycle impact indicator, not the physical mass of minerals used. ADEME and ARCEP quantify material inputs for the wider digital sector, not for AI alone.
Sources: Mistral AI, ADEME and ARCEP