The rapid advancement of artificial intelligence technologies has created an unprecedented challenge in energy consumption and environmental sustainability. This article examines the growing energy crisis in AI computing, analyzing the environmental impact of current AI infrastructure and exploring potential solutions for sustainable development. The article investigates the escalating computational requirements of modern AI systems, particularly in training large language models and data center operations. Through comprehensive analysis of existing literature and recent studies, this article presents emerging solutions including neuromorphic computing, federated learning, edge computing, and quantum approaches. The article also evaluates implementation strategies across various sectors and proposes pathways for achieving sustainable AI development while maintaining operational efficiency. The article highlights the critical need for industry-wide adoption of energy-efficient practices and technological innovations to address the looming energy crisis in artificial intelligence.
Keywords: artificial intelligence sustainability, edge computing, energy-efficient computing, environmental impact assessment, neuromorphic processing
