The ecology of artificial intelligence: energy, water, materials, and land limits of digital systems

Abstract Artificial intelligence (AI) is promoted as a tool for ecological sustainability, yet the ecological effects of AI itself remain poorly understood. Existing studies document aspects of AI’s footprint, including energy demand, water withdrawals, mineral extraction, and land occupation, but treat these impacts in isolation. This article develops an integrated resource nexus framework that situates AI within global environmental systems’ energy–water–material–land metabolism. Drawing on recent evidence, we synthesize quantitative estimates of AI’s resource intensity, from thousands of megawatt-hours and millions of liters required to train frontier models, to embodied mineral and land footprints embedded in semiconductor production and hyperscale data centers. We show that these demands are systemic, entangled, and spatially uneven, with benefits concentrated in the Global North and ecological costs externalized to resource frontiers in the Global South. The contribution of this article is twofold. First, it presents a novel conceptual model of AI as a socio-metabolic infrastructure, highlighting the entropic trade-offs associated with digital expansion. Second, it derives governance pathways, including AI-specific environmental impact assessments, multi-dimensional ecological metrics, and spatially responsible siting strategies that embed AI within planetary boundary frameworks. By reframing AI as an ecological actor with biophysical limits, we advance environmental systems research beyond narrow carbon accounting toward a holistic understanding of digital infrastructures in the Anthropocene.