Exploring the sustainable scaling of AI dilemma: A projective study of
  corporations' AI environmental impacts
Authors: name: Clément Desroches, name: Martin Chauvin, name: Louis Ladan, name: Caroline Vateau, name: Simon Gosset, name: Philippe Cordier

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.