Exploring the sustainable scaling of AI dilemma: A projective study of
  corporations' AI environmental impacts
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.
679a7546805383520ce065af
Clément Desroches
claimed_verified
2025-01-29T21:06:17.418Z
644156da1a80f6d83cb1667c
/avatars/106d30a576b0fb58118ac4333b17260b.svg
Clement Desroches
user
clementdesroches
679a7546805383520ce065b0
Martin Chauvin
claimed_verified
2025-01-30T09:38:17.235Z
66221f6295e8f09a668f07f0
/avatars/f7c943996c814630ab5dcfaaaba01a83.svg
Martin Chauvin
user
Neyri56
679a7546805383520ce065b1
Louis Ladan
679a7546805383520ce065b2
Caroline Vateau
679a7546805383520ce065b3
Simon Gosset
679a7546805383520ce065b4
Philippe Cordier
Exploring the sustainable scaling of AI dilemma: A projective study of
  corporations' AI environmental impacts
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.
The paper presents a methodology to estimate the environmental impact of a company's AI portfolio, highlighting the significant energy consumption of large generative AI models and advocating for coordinated efforts and standardized frameworks to align AI development with net-zero goals.
Large Language Models (LLMs) environmental impact Life-Cycle Assessment (LCA) generative AI models AI electricity use IPCC scenarios hardware efficiency model efficiency Return on Environment