title: Exploring the sustainable scaling of AI dilemma: A projective study of
    corporations' AI environmental impacts
summary: 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.
paper_authors._id: 679a7546805383520ce065af
paper_authors.name: Clément Desroches
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-29T21:06:17.418Z
paper_authors._id: 644156da1a80f6d83cb1667c
paper_authors.avatarUrl: /avatars/106d30a576b0fb58118ac4333b17260b.svg
paper_authors.fullname: Clement Desroches
paper_authors.type: user
paper_authors.user: clementdesroches
paper_authors._id: 679a7546805383520ce065b0
paper_authors.name: Martin Chauvin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-30T09:38:17.235Z
paper_authors._id: 66221f6295e8f09a668f07f0
paper_authors.avatarUrl: /avatars/f7c943996c814630ab5dcfaaaba01a83.svg
paper_authors.fullname: Martin Chauvin
paper_authors.type: user
paper_authors.user: Neyri56
paper_authors._id: 679a7546805383520ce065b1
paper_authors.name: Louis Ladan
paper_authors._id: 679a7546805383520ce065b2
paper_authors.name: Caroline Vateau
paper_authors._id: 679a7546805383520ce065b3
paper_authors.name: Simon Gosset
paper_authors._id: 679a7546805383520ce065b4
paper_authors.name: Philippe Cordier
paper_title: Exploring the sustainable scaling of AI dilemma: A projective study of
    corporations' AI environmental impacts
paper_summary: 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.
paper_ai_keywords: Large Language Models (LLMs) environmental impact greenhouse gas emissions hardware fabrication end-of-life processes Life-Cycle Assessment (LCA) generative AI models electricity use IPCC scenarios AI value chain hardware efficiency model efficiency grid improvements environmental assessment frameworks Return on Environment (ROE)