This article 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 large language model’s (LLM) carbon and water costs, we point out that generative AI technologies are distinctly resource intensive. We argue that the field must reframe 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.