Generative AI poses substantial environmental challenges due to its high energy demands, particularly in training phases. Studies suggest that training models can generate air pollution equivalent to extensive car travel, with significant associated public health costs. Moreover, the energy consumed during generative AI use exceeds that of traditional searches, straining electricity resources and potentially leading to natural habitat loss due to increased water withdrawal in arid areas. Companies must consider these impacts when developing sustainability goals.
Training generative AI is highly energy-intensive, resulting in significant environmental effects, including air pollution, water usage, and stress on electricity resources.
The environmental implications of generative AI often include overlooked costs, such as the increased demand on resources leading to public health costs and habitat loss.
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