I work as a professional app developer. And I find this take to be naive.

Most of the time when I review code from AI, there is always something to improve.

It’s either a maintenance issue. e.g., Opus recommended and implemented a fix for a database corruption crash. This was ~400 lines of code with many moving parts. I reviewed, and found out Android Room library already handles this recovery case, and all I needed was a 10 liner PR that catches this exception and ignores it.

The maintenance is not only the burden on the human and LLM. With too many moving parts, it becomes harder and harder to build and verify the correctness of future features. Yes you can write test for this and that, but it didn’t need to exist in the first place.

The second problem is correctness issues. Especially the edge cases. You cannot just manually test out a race condition on a phone! Sometimes it happens! Sometimes it doesn’t! If it leads to a visible signal like a crash, then yes, you can try to reproduce it. But there are a lot of these that are “silent” and would just lead to bad experiences.

We already had a software quality crisis! And I think such views only exacerbate the situation! Quality matters!

And this is not an anti-AI stance. I vibe code personal projects where I don’t even look at the code. But when I use AI as a professional engineer, I act like a professional. Because these products do have an impact on people’s lives.

So you don't use agentic AI to develop a large mobile app and you think my take is naive?

I also used to work full time as a Android developer for five years, and I'm pretty sure I know better than you about the quality of my app that I work on everyday.

All true (and thanks for posting a concrete example rather than "LLMS suck"). But my take is that none of this is much different than before times when I had teams of developers creating applications. They would often make similar mistakes which I would either need to catch or which would flush out in the field. Where it seems that LLMs are not excellent is where the person driving it is also the senior domain expert so can immediately spot pitfalls. But typically using humans to develop software this was really not often the case. Those people get promoted so they're no longer cutting the code. Under that scenario (replacing subordinate humans) I find the current models are either on-par or somewhat better (specifically because the models can also act like a peer senior dev, discussing approach options etc).