This perspective examines whether nuclear fusion can provide a scalable, low-carbon power source for rapidly growing AI-driven data center demand. As large language models, cloud computing, and cryptocurrency mining accelerate electricity consumption growth, data centers are projected to account for a substantially larger share of U.S. and global electricity use in the coming decades, creating significant pressure on grid reliability and decarbonization goals. We evaluate the technical and economic alignment between data center load profiles and nuclear power, particularly fusion, through a comparative analysis of capacity factors, levelized cost of electricity, grid interconnection constraints, and deployment pathways. Unlike intermittent renewables, nuclear fission and fusion offer high-capacity-factor, firm baseload generation suited to AI training and inference workloads that require continuous, reliable power. Preliminary techno-economic analysis suggests that several Nth-of-a-kind fusion concepts, particularly magnetic confinement systems, may become cost-competitive with firmed renewable systems and advanced fission for hyperscale data center applications. Co-location of fusion plants with data centers further reduces transmission bottlenecks, improves resilience, and aligns with emerging hyperscaler procurement strategies. We also assess recent regulatory developments and argue that fusion's favorable safety profile and reduced waste burden improve its long-term social and political viability relative to fission. We conclude that fusion represents a strategically important pathway for sustainably powering next-generation computing infrastructure and should be prioritized in both policy and industrial deployment planning.
Jakub Kacper SzelÄ…g, Aydin Abadi, Mohammad Naseri
Decentralization as a concept in computer science has existed for over half a century. Despite its fundamental role across domains such as security, distributed computing, artificial intelligence, cloud infrastructures, and Internet of Things (IoT) architectures, there remains no universally accepted definition of decentralization applicable across computer communication systems. This has become increasingly problematic with the emergence of decentralized AI and machine learning paradigms, including collaborative training, distributed inference, blockchain-based, and agentic AI, where decentralization is often treated as a core design objective. Meanwhile, existing approaches frequently conflate decentralization with related notions such as distribution of trust or specific implementation paradigms. Such ambiguity creates inconsistencies in system analysis, limits comparability between works, and weakens the rigor of formal reasoning surrounding communication architectures and protocol design. In this work, we define this research gap as the Decentralization Problem. We analyze the formal-semantic, epistemological, and pragmatic foundations of decentralization and introduce a graph-based ontology defining it as both relational and subject-specific property of computer communication systems. The framework formally distinguishes decentralization from distribution and supports evaluation through two novel metrics: Void Tolerance and Imperviousness. We also provide a browser-based implementation that enables automated classification and metric computation of arbitrary systems. Instantiations to federated learning and blockchain architectures show consistent, comparable assessments where existing definitions produce incomplete or contradictory conclusions, providing a domain-independent foundation for analysing decentralization across heterogeneous systems.