Latency Mismatch in Platformized Journalism and Media: AI-Mediated Access and the Temporal Governance of Cognitive Practices

Abstract

Journalism and media studies increasingly describe a communication environment in which news, explanation and public orientation are accessed through search engines, social feeds, notifications, short-form video, platform summaries, podcasts and conversational artificial intelligence. Existing accounts identify attention scarcity, information overload, platform power, news avoidance and AI disruption, yet they do not fully explain why some communicative practices become less sustainable while faster forms of orientation expand under the same conditions. This conceptual paper develops an integrative theoretical synthesis across media practice theory, deep mediatization, platform studies, digital journalism, digital reading research, cognitive load theory, acceleration studies and AI-mediated communication. It proposes latency mismatch as a middle-range mechanism of temporal selection. Latency mismatch occurs when the time a practice requires to generate stable understanding exceeds the continuity intervals that a media environment makes available, rewards or treats as reasonable, especially when lower-latency alternatives provide sufficient orientation. The concept clarifies how platformized journalism and media environments govern not only visibility and attention but also the durations through which information becomes meaningful, trustworthy and actionable. It reframes long-form news reading, investigative engagement and other high-latency practices as structurally vulnerable but socially necessary conditions of public knowledge.