AI development consumes vast water resources, primarily for cooling servers and manufacturing components. Recent studies estimate that training a single large‑scale model can use 2,000 liters of water, equivalent to the annual consumption of a small household.

Major tech firms are under scrutiny. OpenAI and Google have reported that their data centers consume 100,000 tons of water annually, prompting calls for more efficient cooling technologies.

The environmental cost extends beyond water. Energy‑intensive AI tasks require significant electricity, which often derives from water‑dependent hydroelectric plants or fossil fuels that also demand cooling water.

Industry leaders are exploring alternatives such as liquid‑cooling systems and renewable energy integration to mitigate this impact. The push for greener AI practices is gaining momentum, especially as the sector expands rapidly.