  The growing carbon footprint of artificial intelligence (AI) models,
especially large ones such as GPT-3, has been undergoing public scrutiny.
Unfortunately, however, the equally important and enormous water (withdrawal
and consumption) footprint of AI models has remained under the radar. For
example, training GPT-3 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 may be accountable
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 very concerning, as freshwater scarcity has become one of the
most pressing challenges shared by all of us in the wake of the rapidly growing
population, depleting water resources, and aging water infrastructures. To
respond to the global water challenges, AI models can, and also must, take
social responsibility and lead by example by addressing their own water
footprint. In this paper, we provide a principled methodology to estimate the
water footprint of AI models, and also discuss the unique spatial-temporal
diversities of AI models' runtime water efficiency. Finally, we highlight the
necessity of holistically addressing water footprint along with carbon
footprint to enable truly sustainable AI.
