Comment by noemit

My feeling is that the code it generates is locally ok, but globally kind of bad. What I mean is, in a diff it looks ok. But when you start comparing it to the surrounding code, there's a pretty big lack of coherency and it'll happily march down a very bad architectural path.

In fairness, this is true of many human developers too.. but they're generally not doing it at a 1000 miles per hour and they theoretically get better at working with your codebase and learn. LLMs will always get worse as your codebase grows, and I just watched a video about how AGENTS.md actually usually results in worse outcomes so it's not like you can just start treating MD files as memory and hope it works out.

I don't think that people who don't want to use these tools or clean old ways are incurious. But I think these developers should face the fact that those skills and those ways they are reticent to give up are more or less obviated at this point. Not in the future, but now. It's just that the adoption of these tools isn't evenly distributed yet.

I think there's a place for thoughtful dialogue around what this means for software engineering, but I don't think that's going to change anything at this point. If developers just don't want to participate in this new world, for whatever reason, I'm not judging them, but also I don't think the genie is going back in the bottle. There will be no movement to organize labor to protect us and there be no deus ex machina that is going to reverse course on this stuff.

It's important to point out that you're the one working hard to define AI critics as a camp/group/class when a stronger argument can be made that we're all in the same camp/group/class. I use agentic LLMs for coding every day and I think that it's incredibly important to maintain a critical lens and be open to changing our minds.

However, history suggests that creating artificial divisions is the first step towards all of the the bad things we claim not to like in this world.

Tech adoption generally moves like Time's Arrow. People who use LLMs aren't geeks who changed; we're just geeks. If you want to get off the train, that's your call. But don't make it an us vs them.

Underlying this and similar arguments is the presumption that the "old way" was perfect. You or your colleagues weren't doing one mistake per 100 successful commits. I have been in an industry for decades, and I can tell you that I do something stupid when writing code manually quite often. The same goes for the people that I work with. So fear that the LLM will make mistakes can't really be the reason. Or if it is the reason, it isn't a reasonable objection.

I read the parent comment as calling the majority of AI users "incurious", and not referring to us who resist AI for whatever reasons. The curious AI users can obtain self-improvement, the incurious ones want money or at least custom software without caring how its made.

I don't want the means of production to be located inside companies that can only exist with a steady bubble of VC dollars. It's perfectly reasonable to try AI or use it sparingly, but not embrace it for reasons that can be articulated. Not relevant to parent commenters point, though. Maybe you are "replying" to the article?

Time and time again that I observe it is the AI skeptic that is not reacting with curiosity. This is almost fundamentally true, as in order to understand a new technology you need to be curious about it; AI will naturally draw people who are curious, because you have to be curious to learn something new.

When I engage with AI skeptics and I "ask these people what they're really thinking, and listen" they say something totally absurd, like GPT 3.5-turbo and Opus 4.6 are interchangeable, or they put into question my ability as an engineer, or that I am a "liar" for claiming that an agent can work for an hour unprompted (something I do virtually every day). This isn't even me picking the worst of it, this is pretty much a typical conversation I have on HN, and you can go through my comment history to verify I have not drawn any hyperbole.

you make it seem like ai hesitation is a misunderstood fringe position, but it's not. i don't think anyone is confused about why some people are uninterested in ai tooling, but we do think you're wrong and the defensive posturing lines in the sand come off as incredibly uncurious.

I simply have no need for these things. I am faster, smarter, and I understand more. I syntesize disparate concepts your SoT models could never dream of. Why should I waste the money? I have all that I need up in my brain.

When everyone forgets how to read, I'll be thriving. When everyone is neurotic from prompting-brain, I will be in my emacs, zen and unburdened.

I love that yall have them though, they are kinda fun to mess with. And as long as I can review and reject it, all yalls little generations are acceptable for now.

  > But if you are genuinely confused by the attitudes of your peers, try asking not "what do I have that they lack" ("curiosity"?) but "what do they see that I don't" or "what do they care about that I don't"?

I'd argue these are good questions to ask in general, about many topics. That it's an essential skill of an engineer to ask these types of questions.

There's two critical mistake that people often make: 1) thinking there's only one solution to any given problem, and 2) that were there an absolute optima, that they've converged into the optimal region. If you carefully look at many of the problems people routinely argue about you'll find that they often are working under different sets of assumptions. It doesn't matter if it's AI vs non-AI coding (or what mix), Vim vs Emacs vs VSCode, Windows vs Mac vs Linux, or even various political issues (no examples because we all know what will happen if I do, which only illustrates my point). There are no objective answers to these questions, and global optima only have the potential to exist when highly constraining the questions. The assumptions are understood by those you closely with, but that breaks down quickly.

If your objective is to seek truth you have to understand the other side. You have to understand their assumptions and measures. And just like everyone else, these are often not explicitly stated. They're "so obvious" that people might not even know how to explicitly state them!

But if the goal is not to find truth but instead find community, then don't follow this advice. Don't question anything. Just follow and stay in a safe bubble.

We can all talk but it gets confusing. Some people argue to lay out their case and let others attack, seeking truth, updating their views as weaknesses are found. Others are arguing to social signal and strengthen their own beliefs, changing is not an option. And some people argue just because they're addicted to arguing, for the thrill of "winning". Unfortunately these can often look the same, at least from the onset.

Personally, I think this all highlights a challenge with LLMs. One that only exasperates the problem of giving everyone access to all human knowledge. It's difficult you distinguish fact from fiction. I think it's only harder when you have something smooth talking and loves to use jargon. People do their own research all the time and come to wildly wrong conclusions. Not because they didn't try, not because they didn't do hard work, and not because they're specifically dumb; but because it's actually difficult to find truth. It's why you have PhD level domain experts disagree on things in their shared domain. That's usually more nuanced, but that's also at a very high level of expertise.

But that knowledge was never hidden or out of reach. Why not read books, manuals, or take online classes? There is free access to all these things, the only cost is time and energy.

Everyone has tons of ideas. But every good engineer (and scientist) also knows that most of our ideas fall apart when either thinking deeper or trying to implement it (same thing, just mental or not). Those nuances and details don't go away. They don't matter any less. They only become less visible. But those things falling apart is also incredibly valuable. What doesn't break is the new foundation to begin again.

The bottleneck has never been a shortage of ideas nor the hands to implement them. The bottleneck has always been complexity. As the world advances do does the complexity needed to improve it.

I don't mean to be rude, but you write like a chatbot. This makes sense, to be honest.

So from my perspective as a professional programmer, my feeling is good on you, like, you're empowered to make things and you're making them. It reminds me of people making PHP sites when the web was young and it was easier to do things.

I think where I get really irritated with the discourse is when people find something that works for them, kinda, and they're like "WELL THIS IS WHAT EVERYONE HAS TO DO NOW!" I wouldn't care if I felt like "oh, just a rando on the internet has a bad opinion", the reason this subject bothers me is words do matter and when enough people are thoughtlessly on the hype train it starts creating a culture shift that creates a lot of harm. And eventually cooler heads prevail, but it can create a lot of problems in the meantime. (Look at the damage crypto did!)

You think you know what taste is. Have you been cranking on real systems all these years, or have you been on the sidelines armchairing the theoretics? I'm not trying to come across as rude, but it may be unavoidable to some degree when indirect criticism becomes involved. A laboring engineer has precious little choice in the type of systems available on which to work on. Fundamentally, it's all going to be some variant of system to make money for someone else somehow, or system that burns money, but ensures necessary work gets done somehow. That's it. That's the extent of the optimization function as defined by capitalism. Taste, falls by the wayside, compared to whether or not you are in the context of the optimizers who matter, because they're at the center of the capital centralization machine making the primary decisions as to where it gets allocated, is all that matters these days. So you make what they want or you don't get paid. As an Arts person, you should understand that no matter how sublime the piece to the artist, a rumbling belly is all that currently awaits you if your taste does not align with the holders of the fattest purses to lighten. I'm not speaking from a place of contempt here; I have a Philosophy background, and reaching out as one individual of the Humanities to another. We've lost sight of the "why we do things" and let ourselves become enslaved by the balance sheets. The economy was supposed to serve the people, it's now the other way around. All we do is feed more bodies to the wood chipper. Until we wake up from that, not even the desperate hope in the matter of taste will save us. We'll just keep following the capital gradient until we end up selling the world from under ourselves because it's the only thing we have left, and there is only the usual suspects as buyers.

Ok fella. But show me something then. This is all talk.

Personally I have been able to produce a very good output with Grok in relation to a video. However, it was insanely painful and very annoying to produce. In retrospect I would've much preferred to have hired humans.

Not to mention I used about 50 free-trial Grok accounts, so who knows what the costs involved were? Tens of thousands no doubt.

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Standard AI promotion talking points. Show us the frigging code or presumably your failed slow website that looks like a Bootcamp website from 2014.

This is a fair observation, and I think it actually reinforces the argument. The burnout you're describing comes from treating AI output as "your code that happens to need review." It's not. It's a hypothesis. Once you reframe it that way, the workflow shifts: you invest more in tests, validation scenarios, acceptance criteria, clear specs. Less time writing code, more time defining what correct looks like. That's not extra work on top of engineering. That is the engineering now. The teams I've seen adapt best are the ones that made this shift explicit: the deliverable isn't the code, it's the proof that the code is right.

> I've seen this in myself and colleagues of mine, where LLM use leads to faster burnout and higher cognitive load.

This needs more attention. There's a lot of inhumanity in the modern workplace and modern economy, and that needs to be addressed.

AI is being dumped into the society of 2026, which is about extracting as much wealth as possible for the already-wealthy shareholder class. Any wealth, comfort, or security anyone else gets is basically a glitch that "should" be fixed.

AI is an attempt to fix the glitch of having a well-compensated and comfortable knowledge worker class (which includes software engineers). They'd rather have what few they need running hot and burning out, and a mass of idle people ready to take their place for bottom-dollar.

This is a fair point. The cognitive load is real. Reviewing AI output is a different kind of exhausting than writing code yourself.

Even when the output is "guided," I don't trust it. I still review every single line. Every statement. I need to understand what the hell is going on before it goes anywhere. That's non-negotiable. I think it gets better as you build tighter feedback loops and better testing around it, but I won't pretend it's effortless.

You are correct, but this is not a new role. AI effectively makes all of us tech leads.

Prototyping is a perfectly fine use of LLMs - its easier to see a closer-to-finished good than one that is not.

But that won't generate the returns Model producers need :) This is the issue. So they will keep pushing nonsense.

> But to get the most out of AI you should really be prompting as if you were writing a formal letter to the British throne explaining the background of your request. Basic English writing skills, and the ability to formulate your thoughts in a clear manner, have become essential skills for engineering (and something many developers simply lack).

That's probably why spec driven development has taken off.

The developers who can't write prompts now get AI to help with their English, and with clarifying their thoughts, so that other AI can help write their code.

You are correct. You absolutely must fill the token space with unanbiguous requirements, or Claude will just get "creative". You don't want the AI to do creative things in the same way you don't want an intern to do the same.

That said, I have found that I can get a lot of economy from speaking in terms of jargon, computer science formalisms, well-documented patterns, and providing code snippets to guide the LLM. It's trained on all of that, and it greatly streamlines code generation and refactoring.

Amusingly, all of this turns the task of coding into (mostly) writing a robust requirements doc. And really, don't we all deserve one of those?

> the ability to formulate your thoughts in a clear manner, have become essential skills for engineering

<Insert astronauts meme “Always has been”>

  The art of programming is the art of organizing complexity, of mastering multitude and avoiding its bastard chaos as effectively as possible.

Dijkstra (1970) "Notes On Structured Programming" (EWD249), Section 3 ("On The Reliability of Mechanisms"), p. 7.

And

  Some people found error messages they couldn't ignore more annoying than wrong results, and, when judging the relative merits of programming languages, some still seem to equate "the ease of programming" with the ease of making undetected mistakes.

Dijkstra (1976-79) On the foolishness of "natural language programming" (EWD 667)

I'm currently teaching a group of very curious non-technical content creators at one of the firms I consult at. I set up Codex for them, created the repo to have lots of hand-holding built in - and they took off. It's been 4 weeks and we already have 3 internal tools deployed, one of which eliminated the busy work of another team so much that they now have twice the capacity. These are all things 'real' engineers and product managers could have done, but just empowering people to solve their own problems is way faster. Today, several of them came to me and asked me to explain what APIs are (They want to use the google workspace APIs for something)

I wrote out a list of topics/key words to ask AI about and teach themselves. I've already set up the integration in an example app I will give them, and I literally have no idea what they are going to build next, but I'm .. thrilled. Today was the first moment I realized, maybe these are the junior engineers of the future. The fact that they have nontechnical backgrounds is a huge bonus - one has a PhD in Biology, one a masters in writing - they bring so much to the process that a typical engineering team lacks. Thinking of writing up this case study/experience because it's been a highlight of my career.

Maybe. The reality of software engineering is that there's a lot of mediocre developers on the market and a lot of mediocre code being written; that's part of the industry, and the jobs of engineers working with other engineers and/or LLMs is that of quality control, through e.g. static analysis, code reviews, teaching, studying, etc.

The "most engineers" not "most engineers we've hired".

But also "most engineers" aren't very good. AIs know tricks that the average "I write code for my dayjob" person doesn't know or frankly won't bother to learn.

I wrote code by hand for 20 years. Now I use AI for nearly all code. I just can’t compete in speed and thoroughness. As the post says, you must guide the AI still. But if you think you can continue working without AI in a competitive industry, I am absolutely sure you will eventually have a very bad time.

you’ve been hearing that since mid 2025 bc that’s when it became true.

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