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.

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.

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).

> 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 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 :(

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.

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.

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 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 must be doing something very different from the anti-AI people on here. It is ridiculously empowering.

Got an issue in production? Give your agent the knowledge of how to locate the logs, and where the codebase is, and ask it to diagnose, and off it goes. It almost always finds the issue, and while it has been doing that, I've been able to get on with more productive things.

In terms of coding, if you work on it, and give it the correct guidelines, guardrails and ability to check its own work, it produces very high quality results.

The worst part is in such a short space of time I just don't think I can ever back to normal coding. I don't mind that, but it sucks when I'm offline.

I honestly don't know what people are doing wrong, or what sort of code they're writing that they can't get an AI to work well for them.

Honestly, the effectiveness of LLMs in coding depends a lot on what you're working on. If you're dealing with a software package like Odoo that's been around for literal decades an LLMs output can be borderline useless. The problem is that in its training data it has examples from every version that's ever been released and each succeeding major version makes breaking changes to the previous one, so pretty much what happens is that the LLM can't accurately tell what in its training data belongs to which version before concocting a reply.

>This has not been my experience.

>But the speed that I...

Of course your claim of 5-50x velocity is not born out in any metrics which track industry software velocity and you need to bend yourself backwards to come up with reasons to explain why they aren't.

I’d we are talking about adding code to a large existing production code base then there’s no way I can see to get 5x, let alone 50x. My experience and the data I’ve seen is more like a 10-20% improvement. We see the volume of code increase more than that but a lot of is bug fixes only necessary because the initial commit wasn’t adequately tested and reviewed. So the net effect is less.

Now if you mean generating some one off script or playing around with a prototype in some area you don’t know then I can see more like 5-10x but these are typically not the bottleneck for shipping software.

I'm always wondering who has the time to consume all the new code that is being produced. Like sure, you can produce at 5-10X the speed, but is someone using those features? Not sure if the typical consumer mind can keep up with such speed of changes.

> Guiding them so they deliver good enough code at 5-50x the velocity.

Huge problem with this is the rate at which anyone can take accountability for the code produced.

Of course you can let AI do reviews, but my experience so far is that it's, broadly speaking, not working.

They're expensive to perform and rarely are reproducible but I'll wait for the empirical studies before believing any claims.

We can't even decide if type systems have made us more productive. It's barely been studied. Same with test-driven development.

What it sounds like we'll see, from your description of AI-enabled developers, is a commensurate (perhaps linear) increase in the rate of errors reaching production systems. Every line of code is a liability. Now everyone has a fire-hose they can aim at a production environment.

At least time and effort prevented some bad ideas and potentially bad code from reaching production.

I'm sure the platforms providing these tools are going to be happy with the results when every business writing code this way becomes dependent on them and have no exit strategy. The prices increase, the service gets worse, and you're locked in. Sounds real productive.

This is a business mindset, though. AI is great if you care about "delivering" stuff and "velocity" and you don't care what that stuff looks like. I got into computer programming because I like to program computers, not whatever this is. So glad I changed roles away from software development and only do programming at home as a hobby.

> 50x the velocity

:blinks: You are producing in a week what used to take you a year?

Can someone translate this?

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

What I mean is - are there concrete examples, real world "things" that came from AI programming, that are incredible, and someone can talk about and point to how AI led to the thing being possible?

After going back and forth, I stopped using AI for coding at all.

Maybe I am not "competent" developer, but the point has some merit.

I love AI, but it's possible that we are in an temporary golden age of software development because of 3 things:

1. The software is simple because lowly humans wrote them and debugged them and maintained them.

2. The humans are competent in software engineering.

3. All of a sudden we now have help from AI.

Point 3. is here to stay, but 1. and 2. could disappear.

I feel the same way.

Even if I'm reviewing more, I built the feature without even opening my editor.

My workflow is:

1. Plan mode 2. Read thoroughly or skim if its an easy task 3. /draft command that puts a draft PR on github 4. Review closely then send to team

I'm someone with 20 years in software and the last 10 in management. I have good instincts, good design pattern knowledge, and understand system design well. But my actual coding skills are rusty, I can do it but it takes a lot of time to RTFM because specific libraries and syntax aren't on the top of my mind.

With AI I can build. I'm having so much fun turning ideas into code. I can do a week's worth of work before lunch. I can ask AI to add comments so detailed that my code becomes a refresher tutorial.

It's so exciting to be able to bring my ideas to life, make use of my experience, and not be hobbled by my somewhat atrophied hands-on coding skills. I for one welcome this revolution.

5-50x huh? Years of AI hype and yet still to this day, not a single person or organization can provide any kind of reputable evidence that it has significantly increased their productivity.

One of my engineers used ai yesterday to write a thing without having to "pore over the docs". Engineer and ai decided the third party api was "inconsistent" and "nondeterministic" so they wrote a script to hit it over and over and over hoping to "catch" any "missed" entries. Built a whole cache and mini db to track all the seen IDs as it hammers the api. Luckily I was there to point out how strange it would be for the api to work that way, at which point they read the docs and saw they weren't using the pagination cursor it requires.

Thousands of lines and hours of wasted time and this was the lucky path because a DIFFERENT human happened to be in the loop and asked the right question.

This isn't a general claim about what ai does or doesn't do, but it is a real life anecdote about a very well paid professional.

Someone posted a great quote above that you can outsource your thinking but you can't outsource your understanding.

Well it’s certainly not “clickbait” since the title clearly tells you what it’s about.

> 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

It is difficult for me to read this and believe your brain isn't rotted

If you've become reliant on it, then your skills have atrophied. Your brain has rotted.