Software Developers Say AI Is Rotting Their Brains

I don't think this article is correct exactly, but I do feel that I'm less proud of my work. Less likely to go the extra mile. At first, I tried to do all the due diligence - reading and understanding all of the black box's output. But its clear what my workplace wants - more velocity, more code. If you take time reviewing, you're a blocker. If you lgtm that 3k LoC PR, that's great responsiveness. If you spend two days on a "simple fix" that involves broad cross cutting changes to the system and multiple library updates, you should be doing something else. We are all working across more areas of the system, less specialization, less understanding.

And it is great. It does produce fixes, produce a facimilie of understanding. It answers my questions, and is often right. And tinkering with the process of it is satisfying. Integrating more and more data, writing better specs, you can get better results. Its tempting to think that it could be sustainable, this way of working, but also so scary to lose the understanding, to not have the confidence in how things work. Finding duplicated stacks using different libraries, or even the same library, is becoming more and more common. Even our debugging tools, our tracing grow fragmented and unstandardized.

I liked the old way of working. It was fun for me, if often frustrating. It was solving hard sudoku on the train. This new way is lower friction, but more stress. It's steering a rocket ship using chopsticks to hold the wheel. You desperately want to slow things down and work methodically, to be sure, and safe. But you won't get anywhere near as far if you do that.

Somewhere quiet, the tech debt demon smiles.

"Developers talk not just about how the AI output is often flawed, but that using AI to get the job done is often a more time consuming, harder, and more frustrating experience because they have to go through the output and fix its mistakes."

This has not been my experience. Sure it feels like more work to fix the AI code problems sometimes - it is a different skillset than writing code from scratch. But the speed that I can deliver software has significantly increased by using coding agents.

It's pretty interesting because if you look at the net value over the last 2 to 3 years, you'd expect to see a flurry of high value/high complexity/high velocity software being delivered. But we haven't. We've seen outages almost normalized now and the only new thing being built are more AI integrations - and I have to ask: for whom? To me, there's a pretty big gap between the delivery and claim of AI, and the question is - do you stay an IC or do you become an agent manager (in which case you will lose your technical edge for sure).

You're going to keep seeing this because people don't like AI adoption.

But the fact is this is not how it is. Every competent developer I know is delivering significantly more after being AI enabled.

Anyone seriously using the tools without a chip on their shoulder is going to say the same.

Are the tools delivering perfect code 100% of the time, no, of course not. But that's the new skill. Guiding them so they deliver good enough code at 5-50x the velocity. As the models improve and the ecosystem tries out new workflows, the skill changes and the output gets better and better.

What we're capable of delivering now is incredible and would have been unimaginable just a few years ago.

> At Meta, Google, Microsoft, and others, leadership says that AI generates a growing share of the overall code

Probably because they mandate its adoption. And while there are plenty of developers who will happily comply and see it as a good thing. There are others who will do it because they have to or risk losing their jobs.

It's a bit of a silly thing to claim. "We made everyone use it, so they did, and now adoption is going up!"

Throw away account for obvious reasons. I feel the same way as many. I've been coding for more than 20 years and I've always loved it. The ability to architect and craft something from nothing.

But now with all the vibe coders and agentic coding, I pretty much lost a lot of the interest. I sometimes receive PRs to review of thousands of code where it's clear that they were from some AI and never even tested to begin with, why should I as a reviewer do that for you? If you want to use any AI, at least make sure that it works as it's supposed to, since I'll already have to go through all the code that you didn't write, and likely didn't even read yourself.

Then similarly when I have to build something I sometimes use AI, but it's like cheating, and reducing my coding ability, I can already feel that. But at the end I think that the business just wants that, so I use that, and start to care less about the output, the quality, the whole architecture, so thanks to AI I'm putting less effort, I let AI work for me, while I do other things. Maybe that's the way

I think Andrej Karpathy's quote summarizes well what all software engineers are going through:

,,you can outsource your thinking but not your understanding''

There's just no way to not generate much more amount of code with LLMs than we would do as humans, so well structuring code gets much more important than ever before.

I have just stepped down from a CTO job where I built a FinTech's stack from the ground up. I leveraged a Claude Max plan for about 8 months and I can say with absolute certainty I would not have been as productive without it. I have barely written any code by hand during that time, but I did read almost every single line of code produced. My role was much more that of an "editor", as another article posted here mentioned. There is no doubt in my mind that you can be very highly productive with AI, it's just not the magical silver bullet some people market it as. I have a lot of notes describing the process that I'm considering publishing.

Just yesterday I was interviewing for a very interesting job and I completely flunked the coding question in an unacceptable way for my level of experience. The question was easy, I just couldn't get past some syntactic issues. For 8 months, Claude wrote all of my Python classes and Pydantic types. Now I had to write a dataclass, and because I always just resorted to standard classes before the advent of LLMs, I stumbled. And froze. And panicked. And that was it. Of course you could say I should have just scrapped the dataclass and written it as a simple class. The point is I felt very, very stupid. LLMs suddenly felt like a huge disadvantage.

All this to say I disagree with LLMs "rotting" my brain. Quite the opposite, I know that it's possible to use LLMs to be efficient and correct. It's more the actual mechanical act of writing that gets rusty.

Yesterday I had been talking to an AI all day, and left work with a feeling of non-accomplishment even though I probably did slightly more than I would do normally, though time will tell if the maintenance costs will be higher or not.

And I used to love my work :(

As a senior dev I would say there is a risk of muscle memory atrophy, but the learning opportunities outweigh the risk.

Today I learned about a more elegant helper method in Apache Commons' StringUtils library for Java.

The function was `trimToNull()`

Normally I would have just done

    if (StringUtils.isBlank(foo)) {
        responseDTO.setFoo(null);
    }

Now I can just do responseDTO.setFoo(trimToNull(foo));

I had written the original code, Claude suggested the improvement.

I enjoy shipping code and reviewing what Claude writes.

To add to that, what I find most helpful is the boring stuff, the JIRA cleanup, trawling Wikis and other sources to find out what the historical context of something was.

Normally that would take me all day to do, with a 30 minute code change.

Now I can do that in about 15 minutes and think about building or shipping some tool which I never had time to do.

In my case it's less about actual "rotting" and more about the feeling that any mine attempts to write code are futile and meaningless - if my LLM limits are exhausted it's actually more productive to go do something else (or write specs on how it should be done) and return back later and do LLM assisted coding than coding without it because in literally minutes I can then produce equivalent of hours-long "manual" coding session.

The only use case for AI is for looking up historical references and current events. The latter is probably the most used part, which is why models are only useful if they scrape news sites.

You can also use it for regurgitating manuals, but generative AI for coding is counterproductive. Only the tool and gaming addicted people like it and pretend to be more productive, for which there is no public evidence. I don't see any software improving at any faster rate.

AI agents have made me far more productive, but the work now feels like drudgery. The most intellectually stimulating parts of the job were automated away first, and I am getting increasingly sick of typing into a chat bot all day.

I got into software engineering because I was always fascinated by getting computers to do stuff, and I really enjoyed the manual task of programming. It's been a dream to earn a living doing something I would do in my spare time. I was pretty good at it too.

I'm not having fun any more, so I've decided to leave the field and become a teacher. I won't earn nearly as much money but I expect to feel more fulfilled, and I hope I can help make a difference to some young people.

I've had an extraordinarily privileged career, and many people never get the luxury of enjoying their work at all. But I'd rather try to enjoy what I do day to day than persist in something that's lost its spark.

I have usually positive experience with AI. What it excels in are tasks which are having clear boundaries and proper context.

I have also worked in customer support for some time and I have found that huge problem for some people (often times developers) is that they are lacking theory of mind. Like they literally can't comprehend that I don't see into their heads and they need to articulate their question with correct context otherwise I can't help them.

AI is like a litmus test for it. People who have theory of mind, are capable of putting together a question which will give them good results out of AI. On the other hand people who are struggling with the fact that AI can't see what you mean unless it is in a context window will have bad time with it. These people also usually suck in managing other people because - once again - they are unable to provide tasks with enough context and properly set boundaries. At best they will give you some vague poorly defined tasks and get mad when you will do it differently than they had in their mind.

I wonder how much this is correlated to token budgets? I'd be curious to see a split between $20/$100/$200/$500+ usage and see if there is different responses. I'm in the $400 range with Claude + Cursor subscription, use Opus exclusively, and my experience is wildly different from this.

In the words of the late, great Hal Finney,

"The thing I always love is when there's an intellectual challenge that when you master it gives you practical abilities."

Tangential capabilities from mentally challenging tasks are your personal differentiator - regardless of what tools you use or what you're working on. Running 5 miles puts you way ahead of the person who rode their e-bike for 20. Performing the hard work pays off - not always monetarily, but like exercise, it's uplifting and healthy, and provides long-term advantages.

Corporations need differentiators as well - hopefully they rely on their creative and innovative employees to stand out, regardless of tooling.

Is this substantially different, cognitively or skills-wise, than moving into management and directing a team to write code, but no longer writing code oneself?

The emperor has semi-transparent clothes.

I feel the opposite, but then again, I do not use AI to actually write the code for me. It's like the faster StackOverflow search.

Doesn't open as it is for me.

https://archive.is/2vjJm

Commercial software development will increasingly become dominated by the 'get shit done' types who had less appreciation for the craft. The slop will flow and no one will care, because the people who cared for the craft will have left or been pushed out. A shame.

404 media tends to put out quality articles in my opinion but this one feels a bit like clickbait.

It seems like they're overgeneralizing quite a bit here and focusing on a narrow subset of the population while ignoring the people who are actually thriving with their new AI-enabled dev workflows.

LLMs are not a panacea by any means and they have lots of cons. But I for one would find it difficult to go back to a world where I can't lean on LLMs in my day-to-day.

One very specific example that could not possibly contribute to the brainrot mentioned in this article: AI saves time and reduces the headache of having to pore through pages of documentation (if there even is any) to find how that one method works or what arguments it can take. This alone is immensely helpful and can keep you in a state of flow instead of sending you off on a potentially fruitless side quest that derails your whole train of thought.

It's also taken me quite a bit of time, effort, and experimentation to find the right tools and the right ways to work AI into my workflows which I would bet that the developers mentioned in this article have not explored too deeply if at all.

Claiming AI is rotting your brain because you can't one-shot an entire app or even a single feature is a straw man fallacy.

software developers are crafters. AI turns crafting into production line.

its no wonder that a lot of developers are feeling empty.

I've used LLMs to help with code since before vibe coding was termed.

Experienced mental pains I never felt with any other activity except watching tiktok reels for hours.

Got into points of no returns on numerous side projects, ai slop neither ai or myself could touch.

I've developed a better mental loop. I simply review every lines of code it spits out, and refine the loop to get less code produced. But always demand the full file again.

I commit each change. And inspect the diff for review.

I don't feel drain or pain.

LLMs still aren't standalone developers, but they can be tamed to execute well on well defined scope. If we review what they do, every time.

I'm experiencing the opposite.