Ask HN: Is anyone experiencing "AI brain rot?"

While I was working on this piece about using LLMs to migrate code https://glennengstrand.info/software/llm/migration/java/type... I experienced a first hand encounter of review fatigue. One of the lines of code in the middle of a LLM generated block was gibberish but not gibberish enough to be caught by the compiler. I study the code before accepting the change but just had a complete blind spot on that line. It was the linter that eventually caught it.

I have been "encouraged" by my company to go full throttle into integration LLMs into our workflow. I have found is has taken much of the enjoyment out of solving problems with code. As others have said, it has made me lazier and I can see myself losing my edge.

I'm a neurodivergent coder and have always been a "code by reference" person, so I will have API docs up even if I've done a thing 100 times. The context helps me get into the mindset of what I'm doing, and has helped me be productive.

This fails when people expect rote memorization like in coding interviews, but it works well when I'm actually working through problems in a company - I have a set of references up and available, usually of the internal codebase. I use AI as an advanced reference primarily so I haven't had the problem as stated here yet!

I think a lot of folks are finding that via AI they're switching over to the way that I do code, without the benefits of internal thinking structure I've had to come up with over time to manage it. I try to think of whatever I'm doing in a sort of scaffold, and then change my focus into the details. If you lose your ability to zoom in or out on layers of abstraction (because you don't understand whats happening in detail), that's when you need to spend time diving in.

You can't just sit back all the time like a farmer who's got a machine to plow and tend to all the fields, you need to be doing QA and making sure its doing what it says its doing. If you're letting it handle what the scaffold is, you know even less.

You decide which if any mental models you outsource to AI. You decide what you manage, and whats important for you to manage. Don't let short term productivity gains hobble your ability to zoom in!

No, not really. I find it almost impossible to get AI to generate anything of any significant complexity.

For me the main difference is that I search and read much less when problem solving. In the past I'd often have to search docs and SO for ages looking for information about some bug/functionality. Today about 50% of the time I can just ask AI if it knows what I need. Although the other 50% of the time it will give me some BS hacky answers or just make something up – especially when I'm using less common libraries.

Where I have got much lazier is when it comes to writing BS code like regexs, etc. It's not that I can't write them, I just know that it will take me longer to think about and create the regex than explaining to an LLM what I want to match on. But you still need to know regex to review it so I don't feel like losing any skills, just able to work faster in some cases like how pulling a email regex from SO would have help you code a solution faster a decade a go.

Yes. It's definitely noticeable. Though, I try to console myself by remembering that my architectural design is definitely hypertrophied. My code is ultimately better. I think my LLM-assisted problem solving is definitely more efficient as well. My code literacy is better.

Hopefully LLMs won't vanish away... I'd be at a net negative.