🤖 AI Summary
This study addresses a novel dimension of AI-mediated inequality—termed the Context Access Divide (CAD)—arising from disparities in how users obtain contextual information, specifically between automatic retrieval and manual attachment. Integrating the fan effect from cognitive psychology, the authors develop a probabilistic model demonstrating that in knowledge-intensive tasks, manually attaching context leads to a combinatorial collapse in task success rates as both corpus size and task complexity increase. The work extends analyses of AI inequality beyond individual or organizational levels to the human-AI interaction layer. By leveraging the Model Context Protocol (MCP) within retrieval-augmented generation (RAG) architectures, the study shows that dynamic context retrieval effectively mitigates the exponential decline in success rates associated with larger knowledge bases, thereby uncovering a critical technical root of knowledge work stratification and platform governance.
📝 Abstract
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.