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Source note2 min read

The Context Access Divide: Interaction-Level Architecture as a Complementary Dimension of Agentic Inequality

Source

arXiv

arxiv.org/abs/2607.08495v1

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Plain-Language Brief

The paper proposes a new dimension for analyzing disparities in AI usage called the 'Context Access Divide' (CAD). This divide is based on whether an AI system can autonomously access a user's stored knowledge or requires manual intervention to do so. The CAD is significant for knowledge-intensive workers as it affects the usefulness of AI and could reproduce inefficiencies that AI is intended to eliminate.

What the Document Says

  • The paper introduces the 'Context Access Divide' (CAD) as a new dimension of AI-mediated inequality.
  • The CAD separates users based on whether an AI system can autonomously access their stored knowledge or requires manual intervention.
  • For knowledge-intensive workers, the CAD represents a significant threshold in AI usefulness.
  • The paper suggests that the CAD could reproduce inefficiencies that AI is meant to eliminate.
  • The degree to which an AI system autonomously accesses a user's accumulated knowledge capital is referred to as 'contextuality'.

Key Concepts Mentioned

  • Context Access Divide (CAD)
  • contextuality
  • AI-mediated inequality

Claims Made by the Document

  • The CAD is a significant threshold in AI usefulness for knowledge-intensive workers.
  • Manual context attachment leads to a combinatorial collapse in task-success probability as corpus size and task conjunctivity grow.

Evidence Used

  • Data from the paper's probabilistic model and the fan effect literature in cognitive psychology

Methods / Approach

  • Probabilistic modeling grounded in the fan effect literature in cognitive psychology

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.