The Hidden Cost of AI-Assisted Coding: Why My Brain Feels Busier Than Ever
A reflection on how AI tools changed my coding rhythm — boosting output but fragmenting focus — and how I’m learning to use them efficiently without burning mental energy.
For years, coding felt like flow. I could spend hours deep inside a problem — one continuous train of thought, no friction, no fragmentation. Then came AI-assisted coding. At first, it felt like superpowers: faster code generation, instant debugging, fewer repetitive tasks. But over time, something changed. My days became a blur of tiny jumps — one window for an AI agent, another for ChatGPT, Slack pings in between. I was doing more, but somehow feeling less focused.
I started wondering: is this new productivity actually sustainable?
What’s Going On in the Brain (Briefly)
Every task you do builds a mental setup — what neuroscience calls context. It’s not just memory; it’s your brain’s entire state for a task: goals, rules, active details. When you switch tasks, your brain has to tear that down and build a new one. Each time you jump from coding to chat, or from debugging to prompting an AI, that setup reloads.
Researchers describe this as a reconfiguration process across multiple brain regions involved in attention, memory, and decision-making (Journal of Cognition). It’s a full-system reset — like closing one heavy app and opening another. Do that often enough, and mental fatigue quietly builds up.
The Cost of Constant Switching
Studies show that after even a small interruption, it takes about 23 minutes to return to the original task (Mark, Gudith & Klocke, 2008). Frequent switching can cut effective productivity by 20–40%, and developers who switch tasks more often report slower ramp-up and more errors (Jellyfish Research).
That explains the strange paradox I was feeling: I could finish small things faster — yet big things took longer. AI didn’t slow me down directly. It multiplied micro-interruptions until focus itself became fragmented.
AI Tools: The Double-Edged Boost
AI is brilliant at acceleration — generating, suggesting, fixing. But each tool also adds one more context to manage. Now I might have one agent writing code, another optimizing a query, a Slack thread open, and a ChatGPT tab reasoning about architecture. My brain ping-pongs between them. I’m not just multitasking — I’m multi-contexting.
It’s not about willpower; it’s about bandwidth. Our brains were built for sequential focus, not constant toggling.
Relearning Focus in the AI Era
I’ve started to structure my day differently:
- Batch AI work into dedicated sessions instead of constant prompting.
- Pause intentionally between context changes, even 30 seconds helps.
- Treat prompting as a mode, not a distraction.
- Let AI run while I step away, instead of opening another tab.
In my earlier post, Human Memory, Struggle & AI-Assisted Coding, I wrote about how friction and effort help the brain build lasting memory. That idea applies here too — focus thrives on struggle. When everything becomes instant and AI fills every gap, the challenge isn’t speed; it’s staying engaged enough to remember and learn.
It’s a small shift, but it changes everything. The goal now isn’t just to do more — it’s to stay mentally balanced while doing it.
Closing Reflection
AI made me faster, but also busier. It amplified my output — and my noise. The real challenge now is to use AI in a way that’s truly efficient and mentally sustainable.
In Why Reading the Chain of Thought Matters More Than the Code, I argued that the best results come when we stay inside the reasoning loop — watching how the AI thinks, not just what it produces. That same principle applies to focus: presence matters more than throughput.
"AI gave me more hands, but fewer quiet moments to think. Maybe the next skill to master isn’t prompting — it’s protecting attention."