Claude Code 2 and the hidden cost of slow coding assistants: context switching (coding-with-ai.dev)

The author ran A/B tests between Claude Code 2.0 (powered by Sonnet 4.5) and Codex CLI using GPT-5 (High). While Codex/GPT-5 produced slightly more accurate code, its slower turnaround encouraged the user to switch tasks while waiting, incurring substantial mental "reload" costs. When Claude Code 2.0 returned answers fast enough, the author stayed in the problem, preserved their working memory of file context, hypotheses and recent failures, and moved faster overall despite a small accuracy gap. The piece highlights a concrete, underappreciated trade-off for AI coding assistants: latency is not just a convenience metric but a multiplier on human productivity because of context-switching overhead. For the AI/ML community this means benchmark suites and product design should weigh response time and streaming/interactive behaviors alongside raw accuracy. Human-in-the-loop systems need low-latency, incremental outputs and UI patterns that protect flow state, since small delays can shatter the fragile cognitive graph developers use to reason about code.