探索人工智慧可持續擴展的困境：對企業人工智慧環境影響的預測性研究

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

人工智慧（AI）的快速增長，尤其是大型語言模型（LLMs），引發了對其全球環境影響的擔憂，這超出了溫室氣體排放的範疇，還包括對硬體製造和生命週期過程的考量。主要供應商的不透明度阻礙了企業評估其AI相關環境影響並實現淨零目標的能力。 本文提出了一種方法來估算公司AI投資組合的環境影響，提供可行的見解，而無需大量的AI和生命週期評估（LCA）專業知識。結果證實，大型生成式AI模型的能耗可高達傳統模型的4600倍。我們的建模方法考慮了增加的AI使用量、硬體計算效率以及與IPCC情景一致的電力組合變化，預測AI電力使用量直至2030年。在一個高採用情景下，由廣泛採用生成式AI和代理人所驅動，這些代理人與日益複雜的模型和框架相關聯，AI電力使用量預計將增加24.4倍。 到2030年，減輕生成式AI的環境影響需要AI價值鏈上的協調努力。僅僅依靠硬體效率、模型效率或電網改進的孤立措施是不夠的。我們主張建立標準化的環境評估框架，從價值鏈的所有參與者獲得更大的透明度，並引入“環境回報”指標，以使AI發展與淨零目標保持一致。

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.