The Context Access Divide: Interaction-Level Architecture as a Complementary Dimension of Agentic Inequality
arXiv:2607.08495v1 Announce Type: cross 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 co
The proliferation of AI agents is forcing a deeper examination of emergent inequalities beyond mere access, focusing on the quality and efficacy of user-agent interactions.
This concept refines the understanding of 'agentic inequality,' highlighting that even with ostensible access, differences in interaction architecture can create significant disparities in AI utility and societal impact.
The focus expands from who has access to AI agents, to how effectively individuals and organizations can interact with them, emphasizing architectural design as a key differentiator.
- · AI platform developers prioritizing adaptable interaction architectures
- · Users with high digital literacy and technical understanding
- · Consulting firms specializing in AI integration and optimization
- · Organizations with rigid AI implementations
- · Users lacking technical sophistication or domain-specific context
- · AI developers ignoring human-computer interaction principles
This framework will lead to new metrics and benchmarks for evaluating AI agent effectiveness beyond raw capability.
It will drive demand for user-centric AI design and personalized interaction models to bridge the 'context access divide'.
The recognition of this divide could exacerbate social and economic inequalities if not addressed by equitable AI architectural development, potentially creating a new class of 'AI-empowered' versus 'AI-constrained' individuals.
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Read at arXiv cs.AI