Artificial Intelligence Overload: A Multilevel Taxonomy and the Path Forward

Abstract

As AI becomes a ubiquitous and partially autonomous layer of everyday activity, it increases information volume, interaction tempo, delegated decision-making, and supervisory requirements. This paper introduces “AI overload,” defined as a persistent mismatch between AI-enhanced demands and human and institutional capacity for attention, deliberation, validation, and accountability. We propose a multi-level taxonomy of AI overload across individual, organizational, and societal contexts, comprising seven types: cognitive, informational, interactional, coordination, control, normative, and affective. Exploiting research on cognitive load, automation bias, technostress, and algorithmic mediation, we show how increasing AI agency and human–AI co-adaptation produce new overload pressures. At the societal level, we link AI-driven recommendations and microtargeting with reduced cultural diversity, polarization, and increased susceptibility to manipulation under constrained attention. Finally, we highlight mitigation strategies focused on limited AI autonomy, maintaining control, AI literacy, information access, and complexity-aware modeling to preserve human agency and well-being.