Balancing Innovation and Sustainability: Assessing the Impact of Generative AI on Energy Consumption

Abstract

The rapid advancement of Generative AI is driven by its potential benefits, improvements in computing efficiency, productivity enhancement, organization consolidation of AI's innovations and capability, and limited regulatory oversight. Like many large-scale technology-induced changes, generative AI's current trajectory, characterized by rapid advancements and relentless demand, fails to fully take into account the negative impacts alongside anticipated benefits. Key among these negative impacts is the energy requirement of generative AIs, and the environmental effects are a growing concern. This partial cost-benefit estimation encourages unhindered growth and risk of unfair techno-optimism with possible environmental consequences, together with growing demand for computing power resulting in increasing energy consumption, larger carbon footprints, and accelerated depletion of natural resources, including water resources for cooling.

This necessitates an assessment of the current unsustainable method toward generative artificial intelligence (AI) development and deployment, emphasizing the significance of evaluating the cost-benefit analysis of technological advancements alongside the energy requirement and consequential social and environmental impacts. Currently, efforts to improve computing sustainability largely focus on efficiency enhancements, including refining AI algorithms, boosting hardware energy efficiency, and improving the carbon efficiency of computing workloads. In the existence of expected benefits, relentless demand, and prioritization of economic growth, this focus on productivity improvements results instead of growing adoption without fundamentally taking into account the enormous sustainability implications of generative AIs.

This study posits that striking a balance between innovation and sustainability ensures a brighter future for businesses, the economy, and the planet. Secondly, integrating environmental impact assessments into AI development processes could guide the creation of more sustainable models, ensuring that innovation is not at the detriment of environmental sustainability. Third, the development of sustainable AI models requires focus beyond only efficiency improvements and demands cost-benefits assessment frameworks that promote the development of generative AI models in ways that support social and environmental sustainability goals along with economic opportunity. Fourth, value consideration is multi-layered and needs comprehensive analysis, evaluation, coordination, innovation, and adoption across diverse stakeholders. Fifth, collaboration between governments, international organizations, technology companies, researchers, technical and sociotechnical experts, civil society and other stakeholders is crucial for advancing sustainable AI development practices.