interfaces

Both interruptions and auto-approvals increase with experience. This apparent contradiction reflects a shift in users’ oversight strategy. New users are more likely to approve each action before it’s taken, and therefore rarely need to interrupt Claude mid-execution. Experienced users are more likely to let Claude work autonomously, stepping in when something goes wrong or needs redirection. The higher interrupt rate may also reflect active monitoring by users who have more honed instincts for when their intervention is needed. We expect the per-turn interrupt rate to eventually plateau as users settle into a stable oversight style, and indeed the curve may already be flattening among the most experienced users (though widening confidence intervals at higher session counts make this difficult to confirm).

Respondents found the task less meaningful when they thought that the slogans were AI-generated (Figure 1). Compared to the human-label group, the AI-label group saw a drop in task meaning of about 0.07 standard deviations. Respondents in the AI-label condition were also less willing to contribute a slogan of their own by 3.4 percentage points. Because 26% of participants provided a slogan—and some were remarkably creative—the estimated effect is substantial: a 13% decrease relative to the baseline.

The important ironies of the classic approach to automation lie in the expectations of the system designers, and in the nature of the tasks left for the human operators to carry out.

We conduct randomized experiments to study how developers gained mastery of a new asynchronous programming library with and without the assistance of AI. We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average. Participants who fully delegated coding tasks showed some productivity improvements, but at the cost of learning the library. We identify six distinct AI interaction patterns, three of which involve cognitive engagement and preserve learning outcomes even when participants receive AI assistance. Our findings suggest that AI-enhanced productivity is not a shortcut to competence and AI assistance should be carefully adopted into workflows to preserve skill formation -- particularly in safety-critical domains.

The conclusion I draw is that empathy in these systems is not an inherent state but a manufactured quantity.