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

TL;DR

This paper presents a methodology to estimate AI's environmental impact, revealing that large models consume significantly more energy and emphasizing the need for coordinated efforts and transparency to achieve net-zero targets by 2030.

Contribution

It introduces a practical approach to assess AI environmental impacts without extensive expertise, forecasting future energy use and proposing standardized assessment frameworks.

Findings

Large generative AI models use up to 4600x more energy than traditional models.

AI electricity consumption could increase by a factor of 24.4 by 2030 under high adoption scenarios.

Mitigation requires coordinated efforts across the AI value chain, not isolated measures.

Abstract

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.