Nobody warns you about the adjustment period.
Picking up AI coding assistants like Claude Code and Cursor felt simple enough at first. My plan: offload the tedious work and reclaim time for architecture decisions.
That worked. Sort of.
Boilerplate, unit tests, making sense of a foreign codebase. All of these shrank from 30-40 minutes to roughly five. The speed gain was real. But assuming AI output is production-ready by default? That bit me more than once.
Here's why critical review matters more than any prompting trick. I've seen outputs look polished on the surface, then collapse under edge-case testing. Logic errors hide well until real conditions stress the code.
Treating AI-generated code the same way you'd treat a pull request from a junior dev changes the equation. Slower review, sharper eye, better results.
The other shift worth knowing: context-rich prompts outperform short ones by a wide margin. Instead of "fix this function," describe what it should do, the constraints around it, and what you already tried. The quality gap between a vague prompt and a detailed one is honestly embarrassing.
What I did not expect was where the freed-up mental space would go. Less time writing means more time on system design. That's the right trade for 2026, at least in most cases, where AI coding assistants handle generation and engineers keep judgment.
Start with test generation if you're on the fence. Low risk, immediate payoff, and a solid way to build intuition for where these tools are actually reliable.
