I have a simple way of describing AI code assistants: they are a second pair of eyes, not a replacement for my own. That framing keeps me honest about both sides. Used well, they make me faster. Used carelessly, they make me sloppier, and the damage is quieter because the code still looks confident.
Start with the good, because it is real. An assistant is fast at boilerplate, the repetitive scaffolding that every project needs and nobody enjoys writing by hand. It can sketch out a function, suggest a test, or draft the first version of something I would otherwise type out slowly. That speed matters, because it leaves more of my attention for the design decisions that actually deserve it.
It also works as a reviewer that never sleeps. I can paste in a chunk of code and ask what looks wrong, or ask for a different way to approach a problem. Sometimes the answer is useless. Sometimes it catches something I glossed over, or reminds me of an edge case I had not considered. A second opinion is useful even when it is not always right, as long as I treat it as an opinion and not a verdict.
Now the bad, because it is just as real. The biggest problem is confident mistakes. An assistant will present wrong code in the same smooth tone as correct code, with no hesitation and no warning. If I accept suggestions without reading them, I am not reviewing code. I am collecting it, and the bugs I collect that way tend to be the subtle ones.
Then there is the review burden, which people underestimate. Reading and checking generated code takes time and concentration, sometimes almost as much time as writing it myself. If the code is long or unfamiliar, careful review can erase the speed advantage entirely. The assistant did not remove the work. It moved the work from writing to reviewing, and reviewing is where I have to be most careful.
Skill atrophy is the slower risk. If I let a tool write every loop, every query, and every small function for me, I will gradually lose fluency in the basics. That matters because debugging still requires understanding. When something breaks in production, I cannot ask the assistant to understand my system for me. I need to be able to read the code, reason about it, and fix it myself.
There is also a security angle worth stating plainly. Generated code can contain outdated patterns, unsafe handling of input, or dependencies I have not vetted. I treat assistant output the same way I treat code from any other source I did not write: it goes through the same review, the same tests, and the same suspicion until it earns trust.
Where I land is practical, not ideological. I use assistants for boilerplate, for first drafts, and for a second opinion on code I already understand. I do not use them to skip understanding, and I do not let them make decisions about architecture, security, or data handling without my judgment in the loop. The line is simple: the assistant can suggest, but I decide.
The practical takeaway is to keep yourself in the loop on purpose. Read every suggestion before you accept it, run the tests, and regularly write code without assistance so your own skills stay sharp. An AI assistant is a useful pair of eyes. It is not a substitute for the person responsible for the code, and that person is still me.