Depends on what it’s writing. There are times an LLM saves me a lot of time researching library functionality. Especially with testing frameworks. So many strange and arcane features out there beyond the basics, but not hard to understand what they do once you see the code. On that topic I should say I am careful when reviewing the actual test cases.

However if you’re highly familiar with a domain then LLMs are much less useful.

Most code they write is obviously fine. Much of the rest isn't obviously fine, but is in fact fine once you've gone through understanding it. But yes, there's some that still benefits from a human eye.

(For as long as that's true, "software developer" is still a job. It's not clear for how long it will be true.)

I mean, the reality is a ton of folks in the industry, myself included, are writing glorified CRUD apps in their day jobs. We're building into existing an codebase with established infrastructure and ways of working. What we're building isn't inherently complex or very interesting.

Meanwhile, those codebases often require a ton of boilerplate and drudgery to get anything done.

In these spaces it's very easy to read and comprehend AI generated output and review it fairly quickly. So the time savings from dealing with all that boilerplate and conforming with all that existing infrastructure are potentially substantial.