CONCEPT
Closure Design
Mark's prescriptive framework for incorporating natural endpoints into AI-augmented workflows — addressing the structural absence of completion signals that causes attention residue to accumulate continuously rather than dissipate between tasks.
A conversation with Claude does not end. The worker stops it. But stopping is not ending. The difference matters because the cognitive filing mechanism that releases working memory resources activates on completion, not on cessation. Mark's framework of closure design addresses this structural gap. It prescribes the deliberate incorporation of artificial but psychologically effective endpoints into AI workflows: summary outputs that declare what was accomplished, defined sprints with explicit completion criteria, organizational norms that treat session ends as genuine completions rather than pauses. The intervention is structural, not volitional — it does not ask the worker to feel closure, it engineers the conditions under which closure occurs.
In The You On AI Field Guide
The absence of closure signals in AI interaction has no precedent in human collaboration. A human colleague signals fatigue, has other commitments, ends meetings, goes home. These signals impose boundaries that the worker can use as cognitive breakpoints. An AI tool provides none of them. It does not tire. It