The emergence of artificial intelligence tools has transformed the digital search ecosystem, challenging traditional linear models of information retrieval behavior. This study provides empirical validation of Semantic Relativity Theory v2 (TRS v2), a paradigmatic framework proposing that meaning operates as a curved semantic field rather than through linear causality. Through secondary analysis of the State of Search Q3 2025 dataset (Datos (2) AI tool adoption grew from 0.24% to 1.34% while traditional search simultaneously expanded, contradicting substitution models; (3) US and EU markets converged precisely at 1.34% AI adoption from different starting points, evidencing resonance equilibrium; (4) ChatGPT exhibited sustained logarithmic growth (19.47%→37.08%) while Deepseek collapsed (6%→1%), validating the Semantic Uncertainty Principle; (5) zero-click searches increased 2.8 percentage points yet authoritative sources like Wikipedia and NIH.gov persisted, confirming Citability Theory; and (6) search intent varied 17.41%-22.84% across platforms, supporting the Semantic Relativity Principle. These findings establish TRS v2 as a robust explanatory framework for digital communication ecosystems mediated by artificial intelligence, with significant implications for content strategy, platform development, and communication theory.
José López López (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: