The Energy and Water Footprint of Generative AI

The Energy and Water Footprint of Generative AI: A Vanguard Leadership Perspective

Dr. Tali Režun and Dražen Kapusta, COTRUGLI Business School

The rapid proliferation of generative artificial intelligence (AI) and large language models (LLMs) has ushered in a transformative era for industries. However, it imposes substantial environmental costs through energy and water consumption. This article, grounded in the vanguard leadership framework (VLF), examines the resource demands of generative AI, focusing on energy and water usage, efficiency comparisons among LLMs, and regional implications in Europe, the United States, and Asia. It contrasts AI’s environmental footprint with Bitcoin mining and proposes strategic solutions to mitigate additional energy demands, aligning with the VLF’s adaptive principles. By integrating real-time data, academic insights, and industry trends, the article underscores the urgent need for leaders to harness AI as a force multiplier while navigating its ecological and economic challenges within a three-year window to maintain competitive advantage in Industry 5.0.

Keywords: artificial intelligence, Bitcoin, consumption, disruptive technology, energy,
Industry 5.0, LLM (large language models), technology, vanguard leadership framework

AI power water