The rapid growth of artificial intelligence (AI), particularly Large Language Models (LLMs), has raised concerns regarding its global environmental impact that extends beyond greenhouse gas emissions to include consideration of hardware fabrication and end-of-life processes. The opacity from major providers hinders companies' abilities to evaluate their AI-related environmental impacts and achieve net-zero targets. In this paper, we propose a methodology to estimate the environmental impact of a company's AI portfolio, providing actionable insights without necessitating extensive AI and Life-Cycle Assessment (LCA) expertise. Results confirm that large generative AI models consume up to 4600x more energy than traditional models. Our modelling approach, which accounts for increased AI usage, hardware computing efficiency, and changes in electricity mix in line with IPCC scenarios, forecasts AI electricity use up to 2030. Under a high adoption scenario, driven by widespread Generative AI and agents adoption associated to increasingly complex models and frameworks, AI electricity use is projected to rise by a factor of 24.4. Mitigating the environmental impact of Generative AI by 2030 requires coordinated efforts across the AI value chain. Isolated measures in hardware efficiency, model efficiency, or grid improvements alone are insufficient. We advocate for standardized environmental assessment frameworks, greater transparency from the all actors of the value chain and the introduction of a "Return on Environment" metric to align AI development with net-zero goals.

翻译：人工智能（AI）的快速发展，特别是大型语言模型（LLMs），已引发对其全球环境影响的担忧，这种影响不仅限于温室气体排放，还包括硬件制造和报废处理过程的考量。主要供应商的不透明性阻碍了企业评估其AI相关环境影响及实现净零排放目标的能力。本文提出一种评估企业AI组合环境影响的方法论，无需大量AI及生命周期评估（LCA）专业知识即可提供可操作的见解。研究结果证实，大型生成式AI模型的能耗可达传统模型的4600倍。我们的建模方法综合考虑了AI使用量的增长、硬件计算效率以及符合IPCC情景的电力结构变化，对截至2030年的AI电力消耗进行了预测。在高采用率情景下（由广泛应用的生成式AI和智能体驱动，且伴随日益复杂的模型和框架），预计AI电力消耗将增长24.4倍。要在2030年前缓解生成式AI的环境影响，需要AI价值链各环节的协同努力。仅靠硬件效率、模型效率或电网改进的孤立措施是不够的。我们倡导建立标准化的环境评估框架，提升价值链所有参与方的透明度，并引入"环境回报率"指标，以使AI发展与净零目标保持一致。