Vals Environmental Impacts Report

A report on AI’s environmental footprint argues that model sustainability is poorly measured despite growing public opposition to data-center expansion and emerging regulations requiring environmental disclosures. Using token data from the Vals Index’s approximately 2,157 real-world-style agentic tasks and the EcoLogits estimation library, it evaluates open-weight models’ estimated energy use, carbon emissions, and water consumption during inference, finding that relatively small accuracy gains can entail substantially higher resource demands. Kimi K3 led the benchmark in accuracy but was estimated to require about 1.7 MWh for a full score-weighted run, compared with far lower consumption for several less capable models, while token prices and total task cost were found to be unreliable indicators of environmental impact. A modeled 10-turn chatbot conversation also showed meaningful variation among models, and extrapolating daily use by 66 million US adults suggested sizable aggregate resource consumption. The estimates are not direct measurements and exclude closed models, input and prefill compute, infrastructure variation, cooling advances, and broader local effects such as noise, pollution, and grid strain. The report calls for standardized disclosure of model architecture, hardware use, and data-center resource consumption, alongside local case studies, to support more informed sustainability policy and deployment decisions.

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