From trust to augmentation: A comprehensive survey on synergistic integration of decentralized and generative intelligence

The integration of artificial intelligence (AI) and decentralization is reshaping application and system design across industry, government, and academia. Existing surveys typically examine blockchain, Web3, or generative models independently, which obscures the cross-layer dependencies that govern verifiability, privacy, coordination, and governance in decentralized systems. This survey bridges that gap by introducing a unified trust-to-augmentation framework that organizes the convergence into four interdependent layers: trust-based execution, privacy-preserving interoperable middleware, collaborative learning mesh, and generative augmentation. Unlike prior work that addresses these domains in isolation or in limited binary pairings, the survey explains how advances in one layer alter the requirements and surfaces of the others and identifies research gaps that arise from the integration of decentralized and generative AI. We map representative systems to the four layers and consolidate a taxonomy of enabling techniques, evaluation metrics, and layer-specific comparison tables to support consistent positioning of novel contributions. The synthesis clarifies how the convergence mitigates key limitations of centralized AI, including opacity and single points of failure. It enables automated governance, intelligent consensus, and adaptive user interfaces that preserve fault tolerance and data sovereignty. The analysis also highlights deployment challenges, including scalability bottlenecks, privacy protection under transparent ledgers, cross-chain interoperability, model interpretability, and incentive alignment. The survey identifies barriers to widespread adoption and provides strategic guidance for researchers, practitioners, and policymakers through analysis of real-world applications and deployment methodologies.