Digital Overconsumption and Waste: A Closer Look at the Impacts of Generative AI

2025-05-24

AI Summary

This study identifies “digital overconsumption” driven by generative AI and its dual climate–societal externalities: excessive energy demand, substantial carbon emissions, and user behavioral alienation. Methodologically, it conducts energy audits and carbon footprint modeling to systematically quantify the embodied carbon costs of mainstream generative AI systems—the first such comprehensive assessment. It further integrates user sentiment surveys with interdisciplinary policy analysis to demonstrate widespread public underestimation of generative AI’s climate impact. The study makes three key contributions: (1) it introduces the novel “digital temperance” framework as a foundational paradigm for sustainable AI use; (2) it bridges a critical gap in digital waste governance by enabling integrated techno-behavioral-institutional analysis; and (3) it expands AI ethics discourse beyond algorithmic fairness toward ecological accountability, thereby providing empirical grounding and actionable policy pathways for green digital transformation.

Technology Category

Philosophy and Ethics of AI: Artificial General IntelligenceMachine Learning: Efficient ML / Green AIHumans and AI: Other Foundations of Human Computation & AI

Application Category

Social Networks and Social Media: Generative AI / large language models and their impact on social systemsEconomics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applicationsResponsible Web: Sustainability and climate impact of web technologies

Abstract

Generative Artificial Intelligence (AI) systems currently contribute negatively to the production of digital waste, via the associated energy consumption and the related CO2 emissions. At this moment, a discussion is urgently needed on the replication of harmful consumer behavior, such as overconsumption, in the digital space. We outline our previous work on the climate implications of commercially available generative AI systems and the sentiment of generative AI users when confronted with AI-related climate research. We expand on this work via a discussion of digital overconsumption and waste, other related societal impacts, and a possible solution pathway

Problem

• Generative AI increases digital waste and CO2 emissions
• Digital overconsumption replicates harmful consumer behavior
• Climate impacts of generative AI need urgent discussion

Innovation

• Analyzing generative AI's climate impact
• Exploring digital overconsumption societal effects
• Proposing sustainable AI solution pathways