探讨人工智能可持续扩展困境：对企业人工智能环境影响的前瞻性研究

Exploring the sustainable scaling of AI dilemma: A projective study of corporations' AI environmental impacts

January 24, 2025

作者: Clément Desroches, Martin Chauvin, Louis Ladan, Caroline Vateau, Simon Gosset, Philippe Cordier

cs.AI

摘要

人工智能（AI）的快速增长，特别是大型语言模型（LLMs），引发了对其全球环境影响的担忧，这超出了温室气体排放的范围，还包括对硬件制造和终端处理过程的考虑。主要供应商的不透明度阻碍了公司评估其与AI相关的环境影响并实现净零目标的能力。 本文提出了一种方法论，用于估算公司AI组合的环境影响，提供可操作的见解，无需广泛的AI和生命周期评估（LCA）专业知识。结果证实，大型生成式AI模型的能耗可高达传统模型的4600倍。我们的建模方法考虑了增加的AI使用量、硬件计算效率以及与IPCC情景一致的电力混合变化，预测到2030年AI的用电量。在一个高采用情景下，由广泛采用生成式AI和代理人采用引发的与日俱增的复杂模型和框架相关，预计AI的用电量将增加24.4倍。 到2030年，减轻生成式AI的环境影响需要AI价值链上的协调努力。单独采取的硬件效率、模型效率或电网改进措施是不够的。我们主张采用标准化的环境评估框架，要求AI价值链的所有参与者更加透明，并引入“环境回报”指标，以使AI发展与净零目标保持一致。

English

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.