Carbon Emissions in the Tailpipe of Generative AI
Tamara Kneese, Meg Young · Harvard Data Science Review · 2024
This essay responds to the call for exploring the wider societal risks and impacts of generative AI, particularly its environmental costs. Through a review of the available evidence on LLM’s carbon and water costs, we point out that generative AI technologies are distinctly resource intensive. We argue that the field must re-frame the scope of machine learning research and development to include carbon and other resource considerations across the lifecycle and supply chain, rather than setting these aside or allowing them to remain on the field’s margins.