AI Blockchain Convergence Faces Technical Hurdles and Regulatory Uncertainty

The fusion of artificial intelligence (AI) and blockchain technology has garnered significant attention and investment, with both enthusiasts and skeptics weighing in on its potential.

Despite the hype surrounding AI-blockchain convergence, a closer examination reveals that actual implementations are limited to specific use cases. Decentralized compute networks, on-chain data provenance, and token-incentivised contribution models represent the most coherent applications of this technology.

While these projects demonstrate real-world utility, significant technical hurdles remain. Blockchain's core properties – immutability, decentralisation, and transparency – are architecturally misaligned with what AI systems require at scale. Training large models demands massive, fast data throughput and iterative computation; on-chain computation is orders of magnitude slower and more expensive than equivalent centralized processing.