Developer Experiment Reveals Hidden Latency After AI Code Generation Ends

A developer found that the real performance bottleneck in an AI-assisted coding workflow was not the remote language model but the file-writing steps that followed token generation. Standard product dashboards typically highlight first-token latency, masking heavier stalls that occur during the apply phase. To investigate, the developer built a lightweight Python timer using perf_counter to record named spans and render them as an ASCII waterfall chart. The experiment used a small local JSON fixture with no production traffic, deliberately keeping the setup simple and reproducible. The findings showed that apply-phase operations like writing and fsyncing files produced multiple distinct spans, collectively outweighing the generation span in wall-clock time.