Conceptual analysis demonstrates how ambient AI can support cognitive continuity in digital workflows, highlighting the need for structurally auditable governance.
A conceptual paper arguing that the next meaningful phase of artificial intelligence is not automation or productivity enhancement but the redesign of technological infrastructure to align with how human cognition actually works. It holds that fragmentation is both an incentive problem, produced by architectures optimized for attention extraction, and a structural one, produced by software that requires people to reorganize themselves into rigid taxonomies before they can act — so that much of what is experienced as mental exhaustion is the cost of retrieval and recontextualization rather than of thought. It identifies continuity management as a human function that predates AI, performed by assistants, coordinators, and institutional operators, and argues that AI may scale portions of it without substituting for the judgement it supports. It then argues that as AI becomes ambient rather than interface-bound, governance requirements change in kind: influence that is environmental is harder to perceive and harder to contest, drift becomes gradual rather than visible, and governance must accordingly be inspectable, deterministic where necessary, auditable and structurally enforceable rather than applied after the fact. It closes on the visual legibility of governance and on the architecture in which these requirements are implemented. cognitive infrastructure; ambient AI; attention; fragmentation; continuity management; AI governance; human agency; auditability; ubiquitous computing; Vela Protocol; PromptLedger
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Lara Stuart (2026) studied this question.
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