Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models

TL;DR

This paper investigates the often-overlooked water footprint of AI models, providing a methodology to estimate it and emphasizing the importance of addressing water consumption alongside carbon emissions for sustainable AI development.

Contribution

It introduces a principled methodology for estimating AI's water footprint and discusses the spatial-temporal variations in water efficiency, advocating for holistic sustainability measures.

Findings

AI's water footprint is significant and often secretive.

Global AI water demand could surpass that of entire countries by 2027.

Addressing water footprint is crucial for sustainable AI development.

Abstract

The growing carbon footprint of artificial intelligence (AI) has been undergoing public scrutiny. Nonetheless, the equally important water (withdrawal and consumption) footprint of AI has largely remained under the radar. For example, training the GPT-3 language model in Microsoft's state-of-the-art U.S. data centers can directly evaporate 700,000 liters of clean freshwater, but such information has been kept a secret. More critically, the global AI demand is projected to account for 4.2-6.6 billion cubic meters of water withdrawal in 2027, which is more than the total annual water withdrawal of 4-6 Denmark or half of the United Kingdom. This is concerning, as freshwater scarcity has become one of the most pressing challenges. To respond to the global water challenges, AI can, and also must, take social responsibility and lead by example by addressing its own water footprint. In this paper, we provide a principled methodology to estimate the water footprint of AI, and also discuss the unique spatial-temporal diversities of AI's runtime water efficiency. Finally, we highlight the necessity of holistically addressing water footprint along with carbon footprint to enable truly sustainable AI.