Next-Generation Computing Paradigms for Scalable Artificial Intelligence

Abstract

The rapid growth of Artificial Intelligence (AI) has created unprecedented demands on computing systems. Traditional computing architectures, though powerful, are increasingly limited in their ability to process vast datasets, support complex learning algorithms, and enable real-time decision-making. This paper explores next-generation computing paradigms—such as neuromorphic computing, quantum computing, edge computing, and distributed cloud frameworks—that aim to deliver scalable and energy-efficient AI. By examining the computational bottlenecks of current models and emerging solutions, this study provides a comprehensive understanding of how these paradigms can reshape the future of AI systems.