This makes me a bit suspicious of the article.

Obviously this is just one developers experience now. I've been using Copilot for the last month, and it's an awesome bit of tech. But generally speaking, when I let it write a blob of code:

* There's a 50/50 chance it'll even compile * It's always, _always_ wrong in some way, usually in ways tricky to spot * And the tests it wrote _for it's own code_ are generally wrong too

As always, I think the bottleneck is in describing exactly what you want in a way an AI can successfully interpret it.

That said, copilot is a great tool for generating boilerplate code, like writing a model class for a parsed JWT token or adding tedious annotations somewhere. I've used GPT-3 to generate javadoc headers for a codebase before with decent success. It gets way more fragile when there's an actual problem to be solved though, and is basically unusable about 100% of the time on complex areas of code.

From my experience, the real result will be that AI might replace interns and some people who are only doing the tedious data modeling parts of coding, but there's a very, very long way to go before it will be able to be more than just a tool for developers to code slightly faster.