Your AI has a
hidden water footprint.
Every prompt runs on servers that are cooled with water and powered by plants that consume it. AQUAMIND AI makes that water visible, understandable and actionable.
Based on published figures from the IEA, Google Cloud and the World Resources Institute. Every assumption is open on the methodology page.
What the research says
Published figures from energy agencies, technology providers and water researchers. Each one has its own method and date, which is exactly why AQUAMIND shows its assumptions.
Estimated yearly water consumption linked to data centers, possibly about 1,200 bn L by 2030.
IEA, Energy and AI (2025)Projected data center electricity use in 2030 in the IEA base case, more than double today.
IEA, Energy and AI (2025)Google's estimate of water consumed by its median Gemini text prompt (0.24 Wh), May 2025 data.
Google Cloud, AI inference impact (2025)Share of people in the Middle East and North Africa exposed to extremely high water stress.
WRI, Aqueduct 4.0 (2023)See it, understand it, reduce it
A transparent calculator, a live impact dashboard, a grounded assistant and an academy, all running on the same open model.
From a question to a decision
Five steps that turn an invisible impact into something a student, a sustainability team or a CIO can act on.
Identify
Choose the digital activity: chatbots, images, documents, cloud workloads or organization wide AI.
Estimate
A transparent, deterministic model turns your usage into an estimated water range.
Visualize
See direct versus indirect water, the factors that matter most and how confident the estimate is.
Explore
Compare greener scenarios: cooling, power grid, facility efficiency and usage.
Learn
Ask the assistant and complete Academy modules to turn insight into action.
How much water could your chatbot use?
Making invisible environmental impacts visible through technology, transparency and education.
Our mission, and a future where technological innovation and environmental sustainability advance together.