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

Abstract

This study presents a systematic literature review (SLR) conducted under the PRISMA 2020framework to investigate the convergence of two transformative paradigms: Generative ArtificialIntelligence (GenAI) and Web3. The findings indicate that, while each technology independentlydrives digital transformation, their integration remains underexplored. GenAI advancesinnovation through algorithmic creativity, personalization, and automated content generation,whereas Web3, enabled by blockchain, smart contracts, non-fungible tokens (NFTs), anddecentralized autonomous organizations (DAOs), introduces decentralized mechanisms of trust,transparency, and digital ownership. Current research addressing the intersection of thesedomains 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 unifiedplatform that integrates GenAI-powered content generation, automated tokenization, trustengines, and decentralized marketplaces. This architecture demonstrates cross-sectoral potentialin creative industries, FinTech, and education by linking algorithmic creativity withdecentralized ownership. The contributions are threefold: (1) at the theoretical level, the studysynthesizes the Resource-Based View (RBV), the Dynamic Capabilities View (DCV), the digitaltrust framework, and the information interaction model to establish a foundation for analyzingGenAI–Web3 convergence; (2) at the practical level, it introduces an operational architecture fornext-generation platform development; and (3) at the policy and governance level, it highlightsthe need for transparent, auditable, and participatory models to prevent technological oligopolies.By bridging theoretical insights with practical implications, this research provides a roadmap forfuture scholarship and industry practice, including pilot implementations of the AIChainframework, the design of hybrid governance models, and the assessment of ethical andenvironmental implications surrounding GenAI–Web3 convergence.