I started trying GitHub Copilot this year with a mixture of curiosity and caution.
The demos make it look almost strange at first. You write a comment or start a function, and the editor suggests what might come next. Sometimes it is surprisingly close. Sometimes it is confidently wrong. That combination is what makes it interesting, because the tool can be useful without being something I would trust blindly.
The first thing I wanted to understand was where it actually helped during real work. Not in a demo file, and not in a carefully chosen example. I wanted to see whether it saved time on the ordinary parts of development, the small functions, repeated structures and tests that do not require much creativity but still take attention.
Where It Felt Useful
The most useful moments were small and practical. Copilot was good at suggesting repeated patterns once the surrounding code made the intention obvious. If I was writing a familiar JavaScript helper, a basic PHP function or a test that followed the same structure as the previous one, the suggestion often gave me a starting point.
That changed the feeling of certain tasks. Instead of typing the same shape of code again, I could review a suggestion, adjust it and move on. The important word there is review. The tool might save typing, but it does not remove responsibility. I still need to understand the code that ends up in the project.
I also found it useful for exploring unfamiliar APIs. Not as a replacement for documentation, but as a way of getting an initial idea. If the suggestion looked plausible, I could check it properly. If it looked odd, that was useful too because it made me ask better questions.
Where I Stayed Careful
The risky part is how convincing the suggestions can look. A function can be formatted well, named sensibly and still contain the wrong assumption. That is probably more dangerous than a messy suggestion because it is easier to accept without thinking.
I would not use it as an excuse to skip understanding, especially around security, data handling or anything that affects user accounts, payments or private information. Those areas need deliberate decisions. A generated suggestion might help draft the shape of the code, but it cannot know the full context of the business or the project.
There is also the licensing and source question around AI-generated code, which developers are already discussing. I do not think that conversation can be ignored. For now, I am treating suggestions as something to inspect carefully rather than something to paste into a codebase without thought.
How It Changes The Workflow
The most interesting effect is not that it writes code. The interesting effect is that it changes the rhythm of writing code. It encourages a back-and-forth between intention, suggestion and review. That can be productive, but only if the developer remains in control of the decision.
I found it worked best when I already knew what I wanted. The clearer the surrounding code, the more useful the suggestions became. When I was unsure about the design of a feature, Copilot did not solve that uncertainty. It could generate possibilities, but I still had to decide what belonged in the project.
What I Took From It
Copilot feels like a useful assistant for certain parts of development, not a replacement for judgement. It can reduce some of the typing around familiar patterns, help draft small pieces of code and occasionally point towards an approach worth checking. That is valuable, but it is not magic.
My current view is that the tool is best used by someone who can say no to it. If you understand the problem, you can treat the suggestion as a draft. If you do not understand the problem, the suggestion can make you move faster in the wrong direction. That distinction matters, and I think it will shape how useful these tools become in real projects.