"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.

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.

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.

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.

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 emperor has semi-transparent clothes.

I'm experiencing the opposite.

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.

With AI agents, I'm significantly more productive, but it feels an awful lot more drudgelike to sit and type into a chat bot. For me personally, the most intellectually stimulating parts of the job were automated away first, and I am getting increasingly sick of dealing with project management frenzy and pressing enter all day.

I'm not having fun any more, and 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.

Complaining about this is rather tone deaf, having had an extraordinarily privileged career. Many people do not have the luxury of enjoying what they do to make ends meet. But I've made the calculation that I'd rather at least try and enjoy what I do day to day than persist in this.

> 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!"

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.

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.

Yesterday I have been talking to an AI all day, and left work with a feeling of non-accomplishment even though I probably did 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 :(