(untitled) (Journal of Artificial Intelligence & Research, 2024)
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.