Could an AI assistant interrupt you less often just by paying closer attention to what you're doing?

This explores whether proactive AI help, meaning help that arrives before you ask, can cost less if the AI understands your working context better and its activity is more visible to you. The corpus doesn't test this exact combination directly. It does say clearly where the cost comes from, and that changes what 'reducing it' would have to mean. The key finding is that AI interventions carry a flow cost even when they are correct Does AI assistance always help reasoning or does it carry hidden costs?. A right suggestion at the wrong moment still breaks your concentration, and you then have to rebuild your train of thought. So better context can't only mean better suggestions. It has to mean better timing and better judgment about whether to speak at all. Judging success by whether each suggestion was accurate misses the cost entirely. You have to measure the whole task.

Why is context hard to get right? AI context isn't like the fixed screen state of normal software. It is mutable and short-lived: prompts, history, retrieved data and hidden state shift constantly, and users can't learn it the way they learn a menu layout How does AI context differ from conventional software context?. A related problem is that models don't track what they *don't* know about you. Giving an assistant an explicit list of labeled unknowns about the user cut sycophancy and harmful advice by 50–75% Do language models know what they don't know about users?. That suggests one route to cheaper proactive help: an assistant that knows the gaps in its picture of what you're doing should hold back more often, instead of jumping in with confident but mistimed help.

Visibility works on a different side of the problem. In shared editors, writers strongly preferred seeing when, where and how a collaborator used AI prompts. It helped them understand their partner's thinking and check AI-generated text, though some found full exposure intrusive and made them self-conscious Do writers want to see each other's AI prompts in shared editors?. Visibility doesn't make an interruption cheaper in the moment. It lowers the cost later, of working out what the AI did and whether to trust it. That later cost is real: writers edited AI paragraphs only 23% of the time, and their edits were almost identical to the original text Do writers actually edit AI-generated text before publishing?. Mostly invisible, mostly unedited AI text lets distortions pass straight through. One example is the documented pull of autocomplete toward Western phrasing for Indian writers Do AI writing assistants push non-Western writers toward Western styles?.

The most promising design direction is to make the AI's plan visible and steerable, rather than showing only its output. Cocoa lets users assign individual plan steps to themselves or to an agent, and revise the plan mid-execution. This gave users more control than a chat interface without adding effort Can users steer AI agents better by revising plans mid-execution?. When you can see what the AI intends to do next and claim a step for yourself, its help stops being an interruption and becomes something you schedule.

The surprise is that the costs split in two. Context awareness mainly addresses the interruption cost: when to speak, and whether to speak at all. Visibility mainly addresses the audit cost: what happened, and should I trust it. Most systems optimize only suggestion quality, which touches neither. A short answer to the question is 'probably yes, but only if you design for flow and auditability, not just accuracy.'