Generative AI in the Web3 Era: Systematic Insights into Business Models, Trust, and Innovation

This study presents a systematic literature review (SLR) conducted under the PRISMA 2020 framework to investigate the convergence of two transformative paradigms: Generative Artificial Intelligence (GenAI) and Web3. The findings indicate that, while each technology independently drives digital transformation, their integration remains underexplored. GenAI advances innovation through algorithmic creativity, personalization, and automated content generation, whereas Web3, enabled by blockchain, smart contracts, non-fungible tokens (NFTs), and decentralized autonomous organizations (DAOs), introduces decentralized mechanisms of trust, transparency, and digital ownership. Current research addressing the intersection of these domains is fragmented and predominantly conceptual, leaving critical gaps in trust mechanisms, governance structures, operational models, and legal frameworks. To address these gaps, this study proposes the conceptual AIChain Framework: a unified platform that integrates GenAI-powered content generation, automated tokenization, trust engines, and decentralized marketplaces. This architecture demonstrates cross-sectoral potential in creative industries, FinTech, and education by linking algorithmic creativity with decentralized ownership. The contributions are threefold: (1) at the theoretical level, the study synthesizes the Resource-Based View (RBV), the Dynamic Capabilities View (DCV), the digital trust framework, and the information interaction model to establish a foundation for analyzing GenAI–Web3 convergence; (2) at the practical level, it introduces an operational architecture for next-generation platform development; and (3) at the policy and governance level, it highlights the need for transparent, auditable, and participatory models to prevent technological oligopolies. By bridging theoretical insights with practical implications, this research provides a roadmap for future scholarship and industry practice, including pilot implementations of the AIChain framework, the design of hybrid governance models, and the assessment of ethical and environmental implications surrounding GenAI–Web3 convergence.