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Sharp et al. (2025) explore "agentic inequality" by introducing the Context Access Divide (CAD), which highlights disparities in AI utility based on how context is retrieved during user interactions. They demonstrate that users relying on manual context attachment face a significant decline in task success as the size of their knowledge corpus increases, while those using dynamic context retrieval maintain performance. This finding underscores the importance of contextuality in AI systems, revealing that the method of context access can fundamentally alter the effectiveness of AI for knowledge-intensive tasks.
Users who depend on manual context attachment experience a dramatic drop in task success, revealing a critical divide in AI utility that could reshape how we design intelligent systems.
Sharp et al. (2025) introduce"agentic inequality"as a framework for analyzing disparities in access to AI agents across three dimensions: availability, quality, and quantity. These person- and organization-level dimensions characterize who can access agents and at what capability, but do not address a structurally important divide operating at a finer level: the individual interaction. Two users with nominally equivalent agent access may experience qualitatively different AI utility depending on whether the system can autonomously retrieve context from the user's knowledge corpus (Dynamic Context Retrieval) or requires the user to manually identify and attach relevant documents at each query (Manual Attachment). We term this the Context Access Divide (CAD). For knowledge-intensive workers whose intellectual capital spans tens of thousands of files, the CAD constitutes a qualitative threshold in AI usefulness: below it, the cognitive burden of context curation falls on the human, reproducing the inefficiencies AI is meant to eliminate. We propose contextuality -- the degree to which an AI system autonomously accesses a user's accumulated knowledge capital -- as a dimension of AI-mediated inequality that complements, but is not reducible to, the Sharp et al. framework. We formalize the CAD with a probabilistic model grounded in the fan effect literature in cognitive psychology, demonstrating that manual context attachment leads to a combinatorial collapse in task-success probability as corpus size and task conjunctivity grow, while dynamic retrieval architectures are structurally insulated from this collapse. We analyze the technical basis of this divide in the Model Context Protocol (MCP) and retrieval-augmented generation (RAG) architectures, and examine its implications for knowledge-work stratification and AI platform governance.
