“Using AI for code has reminded me of this sensation… switching in and out of “driving” feels more exhausting than being 100 % one or the other.” You’re not imagining it. There’s a fair bit of cognitive-science literature on task-set inertia: every time you hand work off (human→AI or AI→human) you pay ~100–150 ms to reconstruct the mental model, plus an exponentially-longer “resumption lag” if the state is ambiguous.¹ Do that dozens of times per hour and you’ve effectively added a stealth meeting to your day. A few things that helped me when pairing with an LLM: • Chunk bigger. Treat the AI like a junior dev on 30-minute sprint tickets, not a rubber duck you ping every two lines. • Use “state headers.” I prepend a tiny recap in comments — // you own: parse(), I own: validate() — so I can scan and re-hydrate context instantly. • Declare no-AI zones. Sounds counter-intuitive, but reserving, say, test-writing for uninterrupted solo focus keeps me in flow longer overall. …have you tried anything successfully to address it? We were annoyed enough to build something. At Recontext (YC S24) we sit between your editor and whatever LLM you’re using; every AI request is automatically tagged with the diff, dependency graph, and TODO items so when you jump back in, you get a one-glance briefing instead of spelunking through scrollback. Early users report ~40 % fewer context switches during a coding session. If anyone wants to kick the tires, we’re handing out private beta invites — email is in profile. ⸻ ¹ See Monsell, “Task switching,” Trends in Cognitive Sciences 2003 — the “switch cost” math is sobering.