"Automating the tedious job of writing bugs so you can get on with the important business of investigating them," was a way I jokingly described the value proposition of AI coding assistants to colleagues in the early days of Copilot. It's amusing to see it stated unironically here.
Whether it saves time or not really depends on the task.
The other day, I had a subtitles file with slightly mismatched timestamps. GPT wrote me a Python script to fix them that got 90% of the way there (and, in particular, the code had all the API calls that I needed to get it to 100%, even though this is the first time I've heard of the library it used). The whole thing took less time than finding and installing the app that would do it for me.
The catch is that you need to have a pretty good "gut feel" understanding of its limitations to figure out whether it's going to be a time saver or not before you sink too much time into making it do something right. But it is a skill that can be learned from experience (for a particular model, anyway), and I suspect that the ability to decide what to delegate and what to do yourself will be one of the key differences between junior and senior devs going forward.
I feel similarly to the parent. It gives me a good rough first draft of the code I need, and since I'm using statically typed languages, the errors are generally pretty minor (not giving the right type annotations for example) that it still saves me a lot of time overall from writing everything from scratch.
