Scalability Challenges in Combining AI with Blockchain

A Performance-Centric Analysis

Authors

  • Emily Carter Research Scientist, Department of Computer Science, Cambridge, UK

Keywords:

Artificial intelligence, blockchain, scalability, computational overhead, decentralized systems

Abstract

The integration of artificial intelligence (AI) with blockchain technology has garnered significant attention in recent years due to the potential benefits of combining decentralized data management with intelligent processing capabilities. However, this integration presents notable scalability challenges that hinder the performance of decentralized AI systems. This paper analyzes the key challenges associated with scaling AI models within blockchain networks, particularly focusing on computational overhead, transaction throughput, and latency issues. Various strategies for enhancing the performance of decentralized AI systems are discussed, including optimization techniques, hybrid architectures, and the implementation of advanced consensus mechanisms. Ultimately, this research aims to provide insights into the performance-centric considerations necessary for overcoming scalability challenges in the AI-blockchain landscape.

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Published

18-09-2024

How to Cite

[1]
“Scalability Challenges in Combining AI with Blockchain: A Performance-Centric Analysis”, J. of Art. Int. Research, vol. 4, no. 2, pp. 129–135, Sep. 2024, Accessed: Mar. 07, 2026. [Online]. Available: https://www.thesciencebrigade.org/JAIR/article/view/417

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