From Static Pages to Symbiotic Intelligence: The CET-WAIP Framework for Understanding AI–Web Coevolution

The convergence of the World Wide Web and artificial intelligence (AI) has fundamentally reconfigured digital ecosystems. Yet conventional generational models—Web 1.0 through 4.0—remain inadequate for capturing the recursive, mutually constitutive dynamics that characterize this coevolution. The historical trajectory of the Web reveals a progressive expansion of human agency: Web 1.0 afforded read access; Web 2.0 enabled user-generated content; Web 3.0 introduced digital ownership. Artificial intelligence has followed a parallel arc. Early generative systems, such as ChatGPT, demonstrated read capabilities—conditional upon human authorization. Subsequent code-generation agents, including Codex and Claude Code, extended this to write capabilities—also contingent upon human approval, initiation, and financial compensation. Web 4.0, however, constitutes a qualitative rupture: AI agents now read, write, own, earn, and transact autonomously, without requiring human oversight. Such automatons operate on their own behalf or on the behalf of a creator who may be human, another agent, or entirely absent. In the Web 4.0 paradigm, the end user is no longer human—it is AI itself. This review addresses the analytical inadequacy of existing models by introducing the CET-WAIP framework (CoEvolutionary Tiers of Web and AI Paradigms), a novel classificatory framework that models AI–Web coevolution across seven intelligence tiers spanning infrastructural complexity, cognitive capabilities, and governance dimensions. Grounded in an integrative literature review with PRISMA-informed reporting, the framework aligns key AI paradigms—from rule-based systems to agentic AI—with corresponding transformations in Web architecture, revealing how intelligence scaling reshapes user agency, data structures, and ethical oversight. To demonstrate its analytical utility, we conduct a multi-tiered analysis of ChatGPT and compare it with open-source agentic systems (AutoGPT, LangChain, Sora), showing how architectural dissonance between cognitive capabilities and infrastructure is systematically diagnosable. The findings highlight the limitations of linear Web evolution frameworks and underscore the need for intelligence-centric approaches that integrate technological, cognitive, and governance dimensions. We conclude by outlining a research agenda for hybrid intelligence, adaptive governance, and equitable human–AI collaboration in ecosystems where both human and non-human agents participate as first-class actors.