(untitled) (KELAJAK SARI YANGI O’ZBEKISTON: ILM-FAN, TEXNOLOGIYA VA TA’LIM, 2026)
Artificial intelligence (AI) has environmental impacts that are often overlooked. This paper examines the water footprint of generative AI models by analyzing water consumption associated with data center cooling and electricity generation. It is estimated that approximately 5.4 million liters of water were consumed during the training of GPT-3. In addition, a single ChatGPT query requires about 500 ml of water for every 10–50 responses. Water consumption varies significantly depending on the training location: approximately 15.29 million liters would be required in Washington compared with 3.68 million liters in Virginia. Temporal analysis showed no significant correlation between carbon and water efficiency (Pearson r = 0.06). Potential technical solutions include geographic load balancing, advanced cooling