[striking]

It's not just brain atrophy, I think. I think part of it is that we're actively making a tradeoff to focus on learning how to use the model rather than learning how to use our own brains and work with each other.

This would be fine if not for one thing: the meta-skill of learning to use the LLM depreciates too. Today's LLM is gonna go away someday, the way you have to use it will change. You will be on a forever treadmill, always learning the vagaries of using the new shiny model (and paying for the privilege!)

I'm not going to make myself dependent, let myself atrophy, run on a treadmill forever, for something I happen to rent and can't keep. If I wanted a cheap high that I didn't mind being dependent on, there's more fun ones out there.

[raducu]

> let myself atrophy, run on a treadmill forever, for something

You're lucky to afford the luxury not to atrophy.

It's been almost 4 years since my last software job interview and I know the drills about preparing for one.

Long before LLMs my skills naturally atrophy in my day job.

I remember the good old days of J2ME of writing everything from scratch. Or writing some graph editor for universiry, or some speculative, huffman coding algorithm.

That kept me sharp.

But today I feel like I'm living in that netflix series about people being in Hell and the Devil tricking them they're in Heaven and tormenting them: how on planet Earth do I keep sharp with java, streams, virtual threads, rxjava, tuning the jvm, react, kafka, kafka streams, aws, k8s, helm, jenkins pipelines, CI-CD, ECR, istio issues, in-house service discovery, hierarchical multi-regions, metrics and monitoring, autoscaling, spot instances and multi-arch images, multi-az, reliable and scalable yet as cheap as possible, yet as cloud native as possible, hazelcast and distributed systems, low level postgresql performance tuning, apache iceberg, trino, various in-house frameworks and idioms over all of this? Oh, and let's not forget the business domain, coding standards, code reviews, mentorships and organazing technical events. Also, it's 2026 so nobody hires QA or scrum masters anymore so take on those hats as well.

So LLMs it is, the new reality.

[aftergibson]

This is a very good point. Years ago working in a LAMP stack, the term LAMP could fully describe your software engineering, database setup and infrastructure. I shudder to think of the acronyms for today's tech stacks.

[carimura]

Ya I agree it's totally crazy.... but, do most app deployments need even half that stuff? I feel like most apps at most companies can just build an app and deploy it using some modern paas-like thing.

[dullcrisp]

That was on NBC.

[daxfohl]

Businesses too. For two years it's been "throw everything into AI." But now that shit is getting real, are they really feeling so coy about letting AI run ahead of their engineering team's ability to manage it? How long will it be until we start seeing outages that just don't get resolved because the engineers have lost the plot?

[scorpioxy]

From what I am seeing, no one is feeling coy simply because of the cost savings that management is able to show the higher-ups and shareholders. At that level, there's very little understanding of anything technical and outages or bugs will simply get a "we've asked our technical resources to work on it". But every one understands that spending $50 when you were spending $100 is a great achievement. That's if you stop and not think about any downsides. Said management will then take the bonuses and disappear before the explosions start with their resume glowing about all the cost savings and team leadership achievements. I've experienced this first hand very recently.

[throwup238]

How long until “the LLM did it it” is just as effective as “AWS is down, not my fault”?

[locknitpicker]

> It's not just brain atrophy, I think. I think part of it is that we're actively making a tradeoff to focus on learning how to use the model rather than learning how to use our own brains and work with each other.

I agree with the sentiment but I would have framed it differently. The LLM is a tool, just like code completion or a code generator. Right now we focus mainly on how to use a tool, the coding agent, to achieve a goal. This takes place at a strategic level. Prior to the inception of LLMs, we focused mainly on how to write code to achieve a goal. This took place at a tactical level, and required making decisions and paying attention to a multitude of details. With LLMs our focus shifts to a higher-level abstraction. Also, operational concerns change. When writing and maintaining code yourself, you focus on architectures that help you simplify some classes of changes. When using LLMs, your focus shifts to building context and aiding the model effectively implement their changes. The two goals seem related, but are radically different.

I think a fairer description is that with LLMs we stop exercising some skills that are only required or relevant if you are writing your code yourself. It's like driving with an automatic transmission vs manual transmission.

[bandrami]

Previous tools have been deterministic and understandable. I write code with emacs and can at any point look at the source and tell you why it did what it did. But I could produce the same program with vi or vscode or whatever, at the cost of some frustration. But they all ultimately transform keystrokes to a text file in largely the same way, and the compiler I'm targeting changes that to asm and thence to binary in a predictable and visible way.

An LLM is always going to be a black box that is neither predictable nor visible (the unpredictability is necessary for how the tool functions; the invisibility is not but seems too late to fix now). So teams start cargo culting ways to deal with specific LLMs' idiosyncrasies and your domain knowledge becomes about a specific product that someone else has control over. It's like learning a specific office suite or whatever.

[koiueo]

The little experience I have with LLM confidently shows that LLMs are much better at navigating and modifying a well structured code base. And they struggle, sometimes to a point where they can't progress at all, if tasked to work on bad code. I mean, the kind of bad you always get after multiple rounds of unsupervised vibe coding.

[pards]

> I happen to rent and can't keep

This is my fear - what happens if the AI companies can't find a path to profitability and shut down?

[thevillagechief]

Don't threaten us with a good time.

[satvikpendem]

This is why local models are so important. Even if the non-local ones shut down, and even if you can't run local ones on your own hardware, there will still be inference providers willing to serve your requests.

[MillionOClock]

Recently I was thinking about how some (expensive) customer electronics like the Mac Studio can run pretty powerful open source models with a pretty efficient power consumption, that could pretty easily run on private renewable energy, and that are on most (all?) fronts much more powerful than the original ChatGPT especially if connected to a good knowledge base. Meaning that aside from very extreme scenarios I think it is safe to say that there will always be a way not to go back to how we used to code, as long as we can offer the correct hardware and energy. Of course personally I think we will never need to go to such extreme ends... despite knowing of people who seem to seriously think developed countries heavily run out of electricity one day, which, while I reckon there might be tensions, seems like a laughable idea IMHO.

[Aurornis]

> the meta-skill of learning to use the LLM depreciates too. Today's LLM is gonna go away someday, the way you have to use it will change. You will be on a forever treadmill, always learning the vagaries of using the new shiny model (and paying for the privilege!)

I haven’t found this to be true at all, at least so far.

As models improve I find that I can start dropping old tricks and techniques that were necessary to keep old models in line. Prompts get shorter with each new model improvement.

It’s not really a cycle where you’re re-learning all the time or the information becomes outdated. The same prompt structure techniques are usually portable across LLMs.

[rubenflamshep]

Interesting, I’ve experienced the opposite in certain contexts. CC is so hastily shipped that new versions often imbalance existing workflows. E.g. people were raving about the new user prompt tools that CC used to get more context but they messed my simple git slash commands

[infecto]

I think you have to be aware of how you use any tool but I don’t think this is a forever treadmill. It’s pretty clear to me since early on that the goal is for you the user to not have to craft the perfect prompt. At least for my workflow it’s pretty darn close to that for me.

[Draiken]

If it ever gets there, then anyone can use it and there's no "skill" to be learned at all.

Either it will continue to be this very flawed non-deterministic tool that requires a lot of effort to get useful code out of it, or it will be so good it'll just work.

That's why I'm not gonna heavily invest my time into it.

[rurp]

I have deliberately moderated my use of AI in large part for this reason. For a solid two years now I've been constantly seeing claims of "this model/IDE/Agent/approach/etc is the future of writing code! It makes me 50x more productive, and will do the same for you!" And inevitabely those have all fallen by the wayside and been replaced by some new shiny thing. As someone who doesn't get intrinsic joy out of chasing the latest tech fad I usually move along and wait to see if whatever is being hyped really starts to take over the world.

This isn't to say LLMs won't change software development forever, I think they will. But I doubt anyone has any idea what kind of tools and approaches everyone will be using 5 or 10 years from now, except that I really doubt it will be whatever is being hyped up at this exact moment.

[apercu]

HN is where I keep hearing the “50× more productive” claims the most. I’ve been reading 2024 annual reports and 2025 quarterlies to see whether any of this shows up on the other side of the hype.

So far, the only company making loud, concrete claims backed by audited financials is Klarna and once you dig in, their improved profitability lines up far more cleanly with layoffs, hiring freezes, business simplification, and a cyclical rebound than with Gen-AI magically multiplying output. AI helped support a smaller org that eliminated more complicated financial products that have edge cases, but it didn’t create a step-change in productivity.

If Gen-AI were making tech workers even 10× more productive at scale, you’d expect to see it reflected in revenue per employee, margins, or operating leverage across the sector.

We’re just not seeing that yet.

[prettyblocks]

In my experience all technology has been like this though. We are on the treadmill of learning the new thing with our without LLMs. That's what makes tech work so fun and rewarding (for me anyway).

[Kostic]

I assume you're living in a city. You're already renting out a lot of things to others (security, electricity, water, food, shelter, transportation), what is different with white collar work?

[bondarchuk]

>the city gets destroyed

vs.

>a company goes bankrupt or pivots

I can see a few differences.

[striking]

My apartment has been here for years and will be here for many more. I don't love paying rent on it but it certainly does get maintained without my having to do anything. And the rest of the infrastructure of my life is similarly banal. I ride Muni, eat food from Trader Joe's, and so on. These things are not going away and they don't require me to rewire my brain constantly in order to make use of them. The city infrastructure isn't stealing my ability to do my work, it just fills in some gaps that genuinely cannot be filled when working alone and I can trust it to keep doing that basically forever.