The Generative AI Divide on X: Themes, Frames, and Discursive Shifts in High-Visibility Posts

Generative artificial intelligence (GenAI) is reshaping debates on digital inequality. It shifts attention from simple access gaps to differences in effective use, institutional capacity, and unequal outcomes. This study examines how the “GenAI divide” is constructed in high-visibility discourse on X (formerly Twitter) between 2022 and 2025. The study analyzes a purposively assembled, multilingual but English-dominant corpus of 332 high-engagement posts using an interpretive qualitative design supported by structured descriptive quantification. The posts were retrieved through tool-assisted search, semantic screening, and iterative filtering. Inductive thematic analysis and discourse analysis were used to identify the main dimensions of the debate and the framing, legitimating, and rhetorical strategies through which they circulated. The findings show that high-visibility GenAI divide discourse was organized around three thematic clusters: Access, capability, and distribution; political economy, data, and compute power; and societal and educational impacts. Over time, the discourse shifted from early concerns with paywalls, institutional privilege, and computational concentration toward stronger emphasis on unequal regional availability, labor-market disruption, and broader structural consequences. At the discourse level, early posts relied more heavily on high-certainty and dramatic rhetoric. Later posts increasingly drew on reports, institutional references, and evidence-oriented legitimation. The study suggests that, within high-visibility discourse on X, the GenAI divide is framed not simply as a question of who can access GenAI tools. Instead, it appears as a layered inequality formation shaped by infrastructure, governance, extraction, and unequal capacity to convert access into social and economic advantage.