Habituation at the Gate: Rising Approval and Declining Scrutiny in Human Review of AI Agent Code

As AI coding agents (e.g., GitHub Copilot, Devin, OpenAI Codex, Cursor) submit pull requests to open-source repositories at scale, a key question arises: do human reviewers gradually lower their scrutiny for AI-generated code over time? We conduct a longitudinal within-reviewer analysis using the AIDev dataset, studying 400 repeat reviewers who collectively submitted 11,429 reviews over a seven-month observation period. Comparing each reviewer's early and late review episodes, we observe a population-level shift in approval rate from 30.1% to 36.8% (Wilcoxon signed-rank p < 10^{-6} on paired shifts). Pooled by within-reviewer experience decile, the cumulative gap reaches +14.5 pp from first to tenth decile. This shift is experience-driven (persists after controlling for calendar time), agent-specific (human PR approval rates decline over the same period), and not explained by PR difficulty (median PR size is flat). However, review latency increases rather than decreases (+3.5x), while inline comment volume decreases (-22%, p=0.0014), suggesting reviewers spend more time in queue but less time actively inspecting code. The combination of rising approval, declining comment effort, and increasing queue time is most consistent with reflexive habituation under growing workload rather than rational trust calibration alone.