AI coding assistants can accelerate real work, and dismissing them entirely means leaving value on the table. But an equal and opposite mistake is trusting their output uncritically. These tools can be confidently, plausibly wrong in ways that slip past a casual glance, which makes reviewing their code not optional but essential.
The right posture is to treat generated code as a draft from a fast but fallible collaborator.
Confident and wrong
The particular danger of AI output is that it looks right. Generated code is usually fluent, well-formatted and confident in tone, even when it is subtly incorrect, inefficient, insecure or based on a misunderstanding of your intent. That polish disarms scrutiny, which is precisely what makes the mistakes dangerous — they do not look like mistakes.
A wrong answer that announces its uncertainty is easy to catch. A wrong answer delivered with total confidence is not.
What to check for
Review generated code as you would a human colleague’s pull request, and perhaps more carefully: does it actually do what you intended, does it handle the edge cases and errors, is it secure, and do you understand every line? Shipping code you do not understand is a liability regardless of who or what wrote it, because you cannot maintain or debug what you cannot follow.
Watch especially for plausible-looking logic that is subtly off, and for confident claims about libraries or behaviour that deserve verification.
Staying the engineer
The healthy relationship keeps you as the engineer and the assistant as a tool: it drafts, suggests and accelerates, while you judge, verify and own the result. Understanding what you accept is not a formality — it is what keeps you capable of maintaining the system and catching the confident errors.
Use these tools enthusiastically, but never abdicate judgement to them. The output is a starting point for your thinking, not a substitute for it.