Engineers who dismiss AI
terriblesoftware.org
terriblesoftware.org
For me, however, there is one issue: how can I utilize AI without degenerating my own abilities? I use AI sparingly because, to be honest, every time I use AI, I feel like I'm getting a little dumber. I fear that excessive use of AI will lead to the loss of important skills on the one hand and create dependencies on the other. Who benefits if we end up with a generation of software developers who can no longer program without AI? Programming is not just writing code, but a process of organizing, understanding, and analyzing. What I want above all is AI that helps me become better at my job and continue to build skills and knowledge, rather than making me dependent on it.
With that the students were more directly a match for the in-demand roles, but reality is that other roles will see a reduction of supply.
The question here is: Will there be a need in the future for people who can actually code?
I think so. I also believe the field is evolving and that the pendulum always swings to extremes. Right now we are just beginning to see the consequences of the impact of AI on stability & maintainability of software. And we have not seen the impact of when it catastrophically goes wrong.
If you, together with your AI buddy, cannot solve the problem on this giant AI codebase, pulling in a colleague probably isn't going to help anymore.
The amount of code that is now being generated with AI (and accepted because it looks good enough) is causing long-term stability to suffer. What we are seeing is that AI is very eager to make the fixes without any regard towards past behavior or future behavior.
Of course, this is partially prevented by having better prompts, and human reviews. But this is not the future companies want us to go. They want us to prompt and move on.
AI will very eagerly create 10,000 pipes from a lake to 10,000 houses in need of water. And branch off of them. And again.
Until one day you realize the pipes have lead in them and you need to replace them.
Today this is already hard. With AI it's even harder because there is no unified implementation somewhere. It's all copy pasted for the sake of speed and shipping.
I have yet to see a Software Engineer who stands behind every line of code produced to be faster on net-new development using AI. In fact, most of the time they're slower because the AI doesn't know. And even when they use AI the outcome is worse because there is less learning. The kind of learning that eventually pushes the boundaries in 'how can we make things better'.
Compare ROI of that to being able to get kinda the software you need in a few hours of prompting; it's a new paradigm, progress is (still) exponential and we don't know where exactly things will settle.
Experts will get scarce and very sought after, but once they start to retire in 10-20-30 years... either dark ages or AI overlords await us.
Indeed I believe that, but in my experience these skills get more and more useless in the job market. In other words: retaining such (e.g. low-level coding) skills is an intensively practises hobby of such people that is (currently) of "no use" in the job market.
Couldn't the same statement, to some extent, be applied to using a sorting lib instead of writing your own sorting algorithm? Or how about using a language like python instead of manually handling memory allocation and garbage collection in C?
> What I want above all is AI that helps me become better at my job and continue to build skills and knowledge
So far, on my experience, the quality of what AI outputs is directly related to the quality of the input. I've seen some AI projects made by junior devs that a incredibly messy and confusing architecture, despite they using the same language and LLM model that I use? The main difference? The AI work was based on the patterns and architecture that I designed thanks to my knowledge, which also happens to ensure that the AI will produce less buggy software.
I think the problem the OP is trying to get at is that if we only program at the level of libs we lose the ability to build fundementally cooler/better things. Not everyone does that of course but AI is not generating fundementally new code its copy pasting. Copy Pasting has its limits especially for people in the long term. Copy paste coders don't build game engines. They don't write operating systems. These are esototeric to some people as how many people actually write those things! But there is a craftsmanship lost in converting more people to Copy Paste all be it with inteligence.
I personally lean on the side that this type of abstraction over thinking is problematic long term. There is a lot damage being done on people not necessiarly in Coding but in Reading/Writing especially in (9-12 grade + college). When we ask people to write essays and read things, AI totally short circuits the process but the truth is no one gets any value in the finished product of an essay about "Why columbus coming to the new world cause X,Y or Z". The value is from the process of thinking that used to be required to generate that essay. This is similar to the OPs worry. You can say well we can do both and think about it as we review AI outputs. But human's are lazy. We don't mull over the calculator thinking about how some value is computed something we take it and run. I think there is lot more value/thinking in the application of the calculated results so calculator didn't destroy mathematical thinking but the same is not necessiarly true in how AI is being applied. The fact of your observation of inn junior dev's output proves support to my view. We are short circuiting the thinking. If those juniors can learn the patterns than there is no issue but it's not guarenteed. I think the uncertainity is the the OPs worry but maybe restated in a better way.
Love to hear your thoughts!
My cynical view is you can't, and that's the point. How many times before have we seen the pattern of "company operates at staggering losses while eliminating competition or becoming entrenched in enough people's lives, and then clamps down to make massive profits"?
But at this point, it's like refusing to use vehicles to travel long distances in fear of becoming physicaly unfit. We go to the gym.
This gets particularly tricky when the task requires a competency that you yourself lack. But here too the question is - would you be willing to delegate it to another human whom you don't fully trust (e.g. a contractor)? The answer for me is in many cases "yes, but I need to learn about this enough so that I can evaluate their work" - so that's what I do, I learn what I need to know at the level of the tech lead managing them, but not at the level of the expert implementing it.
That said, as long as there’s the potential for AI to hallucinate, we’ll always need to be vigilant - for that reason I would want to keep my programming skills sharp.
AI assisted software building by day, artisanal coder by night perhaps.
Secondly, bloated comes in so many forms and they all have different reasons. Did you mean bloated as in huge dependency installs like those node modules? Or did you mean an electron app where a browser is bundled? Or perhaps you mean the insane number of FactoryFactoryFactoryBuilder classes that Java programmers have to bear with because of misguided overarchitecting? The 7 layer of network protocols - is that bloating?
These are human decisions - trade-offs between delivering values fast and performance. Foundational layers are usually built with care, and the right abstractions help with correctness and performance. At the app layers, requirements change more quickly and people are more accepting of performance hits, so they pick tech stacks that you would describe as bloated for faster iteration and delivery of value.
So even if I used abstraction as an analogy, I don’t think that automatically implies AI assisted coding will lead to more bloat. If anything it can help guide people to proper engineering principles and fit the code to the task at hand instead of overarchitecting. It’s still early days and we need to learn to work well with it so it can give us what we want.
As humans we have developed tools to ease our physical needs (we don’t need to run, walk or lift things) and now we have a tool that thinks and solve problems for us
This is not a new problem I think. How do you use Google, translator, (even dictionaries!), etc without "degenerating" your own abilities?
If you're not careful and always rely on them as a crutch, they'll remain just that; without actually "incrementing" you.
I think this is a very good question. How should we actually be using our tools such that we're not degenerating, but growing instead?
By writing down every foreign word/phrase that I don't know, and adding a card for it to my cramming card box.
Personally I think my skill lies in solving the problem by designing and implementing the solution, but not how I code day-to-day. After you write the 100th getter/setter you're not really adding value, you're just performing a chore because of language/programming patterns.
Using AI and being productive with it is an ability and I can use my time more efficiently than if I were not to use it. I'm a systems engineer and have done some coding in various languages, can read pretty much anything, but am nowhere near mastery in any of the languages I like.
Setting up a project, setting up all the tools and boilerplate, writing the main() function, etc are all tasks that if you're not 100% into the language take some searching and time to fiddle. With AI it's a 2-line prompt.
Introducing plumbing for yet another feature is another chore: search for the right libraries/packages, add dependencies, learn to use the deps, create a bunch of files, sketch the structs/classes, sketch the methods, but not everything is perfectly clear yet, so the first iteration is "add a bunch of stuff, get a ton of compiler warnings, and then refine the resulting mess". With AI it's a small paragraph of text describing what I want and how I'd like it done, asking for a plan, and then simply saying "yes" if it makes sense. Then wait 5-15m. Meanwhile I'm free to look at what it's doing and if it's doing something stupid wrong, or think about the next logical step.
Normally the result for me has been 90% good, I may need to fix a couple things I don't like, but then syntax and warnings have already been worked out, so I can focus on actually reading, understanding and modifying the logic and catching actual logic issues. I don't need to spend 5+ days learning how to use an entire library, only to find out that the specific one I selected is missing feature X that I couldn't foresee using last week. That part takes now 10m and I don't have to do it myself, I just bring the finishing touches where AI cannot get to (yet?).
I've found that giving the tool (I personally love Copilot/Claude) all the context you have (e.g. .github/copilot-instructions.md) makes a ton of difference with the quality of the results.
I'm not too worried about degrading abilities since my fundamentals are sound and if I get rusty due to lack of practice, I'm only a prompt away from asking my expert assistant to throw down some knowledge to bring me back up to speed.
Whilst my hands on programming has reduced, the variety of Software I create has increased. I used to avoid writing complex automation scripts in bash because I kept getting blocked trying to remember its archaic syntax, so I'd typically use bun/node for complex scripts, but with AI I've switched back to writing most of my scripts in bash (it's surprising at what's capable in bash), and have automated a lot more of my manual workflows since it's so easy to do.
I also avoided Python because the lack of typing and api discovery slowed me down a lot, but with AI autocomplete whenever I need to know how to do something I'll just write a method stub with comments and AI will complete it for me. I', now spending lots of time writing Python, to create AI Tools and Agents, ComfyUI Custom Nodes, Image and Audio Classifiers, PIL/ffmpeg transformations, etc. Things I'd never consider before AI.
I also don't worry about its effects as I view it as inevitable, with the pendulum having swung towards code now being dispensable/cheap to create, what's more important is velocity and being able to execute your ideas quickly, for me that's using AI where I can.
The shareholders benefit this quarter. Look man, I know you probably have a high opinion of yourself and all, but your job now is to degrade your abilities in order to deliver faster results now. The investors kindly demand that you get with the program, enthusiastically accept your new role as a depreciating asset (not human capital to be invested in), and stop thinking so much.
Apologists love to make such analogies. "From 30,000 feet, doesn't the new things kinda look like some old thing? Then they're the same and you should accept the new thing!" But the analogies are never apt, and the "argument" is really only one of glossing over the differences.
The whole point of AI is for people to think less. It's basically the goddamned name. If people aren't thinking less, AI isn't doing its job. All of those things you listed are instances of mechanical translation, and aren't thinking..
> You can put a strong local coding model on your local hardware today and no investor will be involved (unless you mean investors on the company you work for, but the truth is,n those were never in any way interested in how you build things, only that you do).
Don't pretend you can cosplay a capitalist with AI. You need money, and if you can build something with a local model, the people with money can do it too, so they don't have to pay you. We work for a living.
Also it's a fantasy your local model with be anything but a dim candle to the ones the rich have. Real life is not a sci-fi novel.
Your employers are hoping to use you up making this current imperfect iteration of the technology work, because the hope is the next version won't need you. Don't be cheerful about it. It's a bad end for you.
Will what I do now be automated eventually or before long? Probably, we keep automating things, so one has to swim up the abstraction layers. Doesn't mean one has to think less.
To use them well you still need to know everything - whenever you prompt lazily you're opening yourself up to a fuckton of technical debt.
That might be acceptable to some, but is generally a bad idea
If it was AGI, you'd be right though...
> That might be acceptable to some, but is generally a bad idea
That's my point: to use them effectively you need to know everything, but to use them heavily puts you in a situation where that knowledge atrophies (e.g. the OP's statement "every time I use AI, I feel like I'm getting a little dumber"). The bosses want the results now, and don't mind if in a few years you're much less capable (and maybe not capable of getting effective results anymore).
If AGI comes soon enough, their bet will have paid off.
"Coding in the Red-Queen Era" https://corecursive.com/red-queen-coding/ (2025)
So what's my cut of something basically worthless? Doesn't seem lucrative in the long run.
Prices are not fundamental truths. They’re numbers that happen to work. Ideally price > cost, but that’s not even reliably true once you factor in fixed costs, subsidies, taxes, rebates, etc. Boeing famously came out and said they couldn't figure out how much it actually cost to make a 747, back when they were still flying.
Here's a concrete example: You have a factory with $50k/month in fixed costs. Running it costs $5 per widget in materials and labor. You make 5,000 widgets.
Originally you sell them for $20. Revenue $100k, costs $75k, pocket a cool $25k every month. Awesome.
Then, a competitor shows up and drives the price down to $10. Now revenue is $50k. On paper you “lose money” vs your original model.
But if you shut the factory down, you still eat the full $50k fixed cost and make $0. If you keep running, each widget covers its $5 marginal cost and contributes $5 toward fixed costs. You break even instead of losing $50k.
That’s the key mistake in "AI output is worth zero." Zero marginal value does not imply zero economic value. The question is whether it covers marginal cost and contributes to something else you care about: fixed costs, distribution, lock-in, differentiation, complements, optionality.
We've faced this many times before so AI isn't special in this regard. It just makes the gap between marginal cost and perceived value impossible to ignore.
> They go on to discuss what is good or bad in writing. Socrates tells a brief legend, critically commenting on the gift of writing from the Egyptian god Theuth to King Thamus, who was to disperse Theuth's gifts to the people of Egypt. After Theuth remarks on his discovery of writing as a remedy for the memory, Thamus responds that its true effects are likely to be the opposite; it is a remedy for reminding, not remembering, he says, with the appearance but not the reality of wisdom. Future generations will hear much without being properly taught, and will appear wise but not be so, making them difficult to get along with.
They dismiss the religion like hype machine.
You want to market to engineers, stick to provable statements. And address some of their concerns. With something other than "AI is evolving constantly, all your problems will be solved in 6 months, just keep paying us."
Oh by the way, what is the OP trying to sell with these FOMO tactics? Yet another ChatGPT frontend?
You download a tool written by a human, you can reasonably expect that it does what the author claims it does. And more, you can reasonably expect that if it fails it will fail in the same way in the same conditions.
I wrote some Turing Machine programs back in my Philosophy of Computer Science class during the 80's, but since then my Turing Machine programming skills have atrophied, and I let LLMs write them for me now.
Really though, the potential in this tech is unknown at this point. The measures we have suggest there's no slowdown in progress, and it isn't unreasonable for any enthusiast or policy maker to speculate about where it could go, or how we might need to adjust our societies around it.
What is posted to HN daily is beyond speculation. I suppose a psychologist has a term for what it is, I don't.
Edit: well, guess what? I asked an "AI":
Psychological Drivers of AI Hype:
Term | Driver | Resulting Behavior
-----------------------|-----------------------|------------------------------
ELIZA Effect | Symbolic projection | Treating a script like a person.
Automation Bias | Cognitive offloading | Trusting AI over your own eyes.
Techno-Optimism | Confirmation bias | Ignoring risks for "progress."
Interface Familiarity | Fluency heuristic | Friendly UI = "Smart" system.
And that's where the "AI" is lacking.
"AI can write a testcase". Can it write a _correct_ test case (i.e. one that i only have to review, like i review my colleague work) ?
"AI can write requirements". Now, that i'm still waiting to see.
And is the test case useful for something? On non textbook code?
One does not need to embrace a tool to recognize its horrendous effects and side-effects. I can critique assault rifles without ever having handled one. I can critique street narcotics without taking drugs, and I can critique nuclear weapons without suffering from a blast personally. The idea that if you don't use a tool you can't form conclusions about why it's bad is devoid of any factual or historical grounding. It's an empty rhetorical device which can be used endlessly.
Literally 100% of the inventions which come out now or in the future can be handled in the exact same way. Somebody invents a robot arm that spoon feeds you so you never need to feed yourself with your own hand ever again? Oh this is revolutionary, everybody's going to install this in their home! What, you haven't? And you think it's a bad idea? Gosh, you're so backwards and foolish. The world is moving on, don't be left behind with your manual hand feeding techniques.
This article is like 1.5 years out of date. The discourse around genAI as a tech movement and its nearly uniformly terrible outcomes has moved on. OP hasn't. Seems the gap is widening between writers who are talking about these tools soberly and seriously, and writers who aren't.
The OP doest't even deserve your sincere and just to the point response.
Especially the part about somebody completing a project quickly being depicted as "cheating". Absurd.
But, to be fair, that wasn't the kind of critique it was talking about. If your critique guns is moral, strategic, etc, then yes, you can do it without actually trying out guns. If your critique is that guns physically don't work, don't actually do the thing they are claimed to do, then some hands-on testing would quickly dispel that notion.
The article is talking about those kinds of critiques, ones of the "AI doesn't work" variety, not "AI is harmful".
These tools are seriously starting to become actually useful, and I’m sorry but people aren’t lying when they say things have changed a lot over the last year.
Sometime the result is not great, sometimes it requires manual updates, sometimes it just goes into a wrong direction and we just discard the proposal. The good thing is you can initiate such a large change, go get a coffee, and when you're back you can take a look at the changes.
Anyway, overall those tools are pretty useful already.
I've been at it multiple decades. TC $1M+. Forever beginner I guess.
https://youtu.be/1OzxYK2-qsI a 6-12% increase in pull requests per developer per month.
"But those diffs are AI slop and rework" you will object. Oh well, I tried.
Homeopathy works for sure. Placebo works. There are many studies confirming that.
It doesn't matter if you're using AI or not, just like it never mattered if you were using C or Java or Lisp, or using Emacs or Visual Studio, or using a debugger or printf's, or using Git or SVN or Rational ClearCase.
What really matters is in the end is, what you bring to market, and what your audience thinks of your product.
So use all the AI you want. Or don't use it. Or use it half the time. Or use it for the hard stuff, but not the easy stuff. Or use it for the easy stuff, but not the hard stuff. Whatever! You can succeed in the market with AI-generated product; you can fail in the market with AI-generated product. You can succeed in the market with human-generated product; you can fail in the market with human-generated product.
Imagine investing tons of efforts and money into a startup, just to get a clone a week after launch, or worse - before your launch.
- google maps
- power tools
- complex js frameworks
- ORMs
- the electrical grid (outages are a thing)
- and so on…
This isn’t a new problem unique to LLMs.
Practice using the tool intelligently and responsibly, and also work to maintain your ability to function without when needed.
It's full of bloat; Unused http endpoints, lots of small utility functions that could have been inlined (but now come with unit tests!), missing translations, only somewhat correct design...
The quality wasn't perfect before, now it has taken a noticeable dip. And new code is being added faster than ever. There is no way to keep up.
I feel that I can either just give in and stop caring about quality, or I'll be fixing everyone else's AI code all of my time.
I'm sure that all my particular colleagues are just "holding it wrong", but this IS a real experience that I'm having, and it's been getting worse for a couple of years now.
I am also using AI myself, just in a much more controlled way, and I'm sure there's a sweet spot somewhere between "hand-coding" and vibing.
I just feel that as you inch in on that sweet spot, the advertised gains slowly wash away, and you are left with a tangible, but not as mindblowing improvement.
In my line of work, I keep seeing it generate sloppy state machines with unreachable or superfluous states, bad floating-point arithmetic, and especially trying to do everything in the innermost loop of a nested iteration.
It also seems to love hallucinating Qt features that should exist but don’t, which I find mildly amusing.
There is also a big, uncomfortable truth regarding "AI" coding tools: They are trained on open-source code, yet they ignore the licenses attached to that code. If it's unethical for me to copy-and-paste MIT licensed code without including the license text, then it's unethical to let an LLM do it on my behalf.
LLMs are paving the way to a dystopia where there's no motivation for humans to create, and that world sounds miserable.
My aunt was born in the 1940s, and was something of an old fashioned feminist. She didn't know why wasn't allowed to wear pants, or why she had to wait for the man to make the first move, etc. She tells a story about a man who ditched her at a dance once because she didn't know the "latest dance." Apparently in the 1950s, some idiot was always inventing a new dance that everyone _just had follow_. The young man was so embarrassed that he left her at the dance.
I still think about this story, and think about how awful it would have been to live in the 40s. There always has been social pressure and change, but the "everyone's got to learn new stupid dances all the time" sort of pressure feels especially awful.
This really reminds me of the last 10-20 years in technology. "Hey, some dumb assholes have built some new technology, and you don't really have the choice to ignore it. You either adopt it too, or are left behind."
Predicting the future, I can tell you with certainty that This Too Shall Pass.
Just don't ask me what 'This' is: A fad, or a sea-change?
My main Luddite objection to the current passion for LLM coding assistance is the new dependencies created/fostered within the software engineering diaspora. Not only are we dependent now on cloud access for so much of our SE now, but we'll also depend on the ongoing build-out, as LLM infrastructure bulldozes our real-world landscape, and we experience change from DRAM shortages to unwanted (NIMBY) construction of data centers. All based on the idea (yet again) that this is it.
The question becomes, is it (or will it be) worth it?
My personal prism of past experience does not lend itself to an easy answer. The move from assembly language to C was a no-brainer, but I remember resisting a transition from C to C++ ("I can do that in C with structs and function pointers") and the surge in OOP and COM and... the list goes on.
I remember objecting to Rational Rose and UML because I just didn't trust code generated algorithmically. Boilerplate with artifacts. I don't think I was wrong to hesitate there.
But I might be wrong now, to let others push the leading bleeding edge. Maybe it's time to get into it.
Can I just download a trained LLM and host it myself, without a dependency on internet/cloud corporate/overlord/rented-infrastructure?
I am willing to try, but I must declare that-- where I am, the Personal Computing revolution is not over. We still haven't won. And the rebellion: against auto-updates, telemetry, subscription models, any usage or dependency of/on your internet against your will. The fight for freedom goes on.
Can I get a Claude Code to live in my home with me, air-gapped and all mine?
Will I spend all my time spanking the agent?
Honest question for the engineers here. Have you seen this happening at your company? Are strong engineers falling behind when refusing to integrate AI into their workflow?
A guy will proudly deploy something he vibe coded, or “write the documentation” for some app that a contractor wrote, and then we get someone in the business telling us there’s a bug because it doesn’t do what the documentation says, and now I’m spending half a day in meetings to explain and now we have a project to overhaul the documentation (meaning we aren’t working on other things), all because someone spent 90 seconds to have AI generate “documentation” and gave themselves a pat on the back.
I look at what was produced and just lay my head down on the desk. It’s all crap. I just see a stream of things to fix, convention not followed, 20 extra libraries included when 2 would have done. Code not organized, where this new function should have gone in a different module, because where it is now creates tight coupling between two modules that were intentionally built to not be coupled before.
It’s a meme at this point to say, ”all code is tech debt”, but that’s all I’ve seen it produce: crap that I have to clean up, and it can produce it way faster than I can clean it up, so we literally have more tech debt and more non-working crap than we would have had if we just wrote it by hand.
We have a ton of internal apps that were working, then someone took a shortcut and 6 months later we’re still paying for the shortcut.
It’s not about moving faster today. It’s about keeping the ship pointed in the right direction. AI is a guy a guy on a jet ski doing backflips, telling is we’re falling behind because our cargo ship hasn’t adopted jet skis.
AI is a guy on his high horse, telling everyone how much faster they could go if they also had a horse. Except the horse takes a dump in the middle of the office and the whole office spends half their day shoveling crap because this one guy thinks he’s going faster.
One nice change however is that you can guide the latter towards a total refactor during code review and it takes them a ~day instead of a ~week.
I also find that the actual coding is important. The typing may not be the most ineresting bit, but it's one of the steps that helps refine the architecture I had in my head.
That happens to produce good code as a side effect. And a chat bot is perfect for this.
But my obsession is not with output. Every time I use AI agents, even if it does exactly what I wanted, it’s unsatisfying. It’s not sometning I’m ever going to obsess over in my spare time.
I feel like I'm living in a different world when every time a new model comes out, everyone is in awe, and it scores exceptionally well on some benchmark that no one heard of before before the model even launched. And then when I use it, it feels like it's exactly the same as all models before, and makes the same stupid mistakes as always.
What helps me is:
- Prefer faster models like VSCode's Copilot Raptor Mini which, despite the name, is like 80% capable of what Sonnet 4.5 is. And is much faster. It is a fine tunned GPT 5 mini.
- Start writting the next prompt while LLMs work or keep pondering about the current problem at hand. This helps our chaotic brain to keep focused.
I am one of those dismissers. I am constantly trash talking AI. Also, I have tried more tools and more stress scenarios than a lot of enthusiasts. The high bars are not in my head, they are on my repositories.
Talk is cheap. Show me your AI generated code. Talk tech, not drama.
If you disagree with the above statement, try replacing "AI" with "Docker", "Kubernetes", "Microservices architecture", "NoSQL", or any other tool/language/paradigm that was widely adopted in the software development industry until people realized it's awesome for some scenarios but not a be-all and end-all solution.
How many people are actually saying this? Also how does one use modern coding tools in heavily regulated contexts, especially in Europe?
I can't disagree with the article and say that AI has gotten worse because it truly hasn't, but it still requires a lot of hand holding. This is especially true when you're 'not allowed' to send the full context of a specific task (like in health care). For now at least.
void secp256k1_gej_add_gej(secp256k1_gej *r, const secp256k1_gej *a, const secp256k1_gej *b);
void secp256k1_gej_add_gej_var(secp256k1_gej *r, const secp256k1_gej *a, const secp256k1_gej *b);
But neither can most humans.
I admit to being surprised at what it actually can do, pretty much all by itself.
https://arstechnica.com/ai/2025/12/the-ars-technica-ai-codin...
I don't know what other programmers are doing, but a lot of my time is spent on tasks like this.
Here's another random task: Write an analytic ray - cubic Bézier patch intersection routine based on the based on the "Ray Tracing Parametric Patches", SIGGRAPH 82 paper. This is a task I did as part of my final project for my undergraduate graphics class.
These are both straightforward tasks to take well-described existing algorithms from literature and implement them concretely. Very few design choices to consider. In theory it ought to be right up the alley for what AI is supposedly good for.
For what it’s worth, I’m fine with “falling behind.” I didn’t want to be a manager when I was at FAANG, and I don’t want to be an AI puppetmaster now. I got into this field because I actually love programming, and I’ll just keep on doing that while the world gets driven mad by their newfound corporate oracle, thanks. Feel free to lap me with your inscrutable 10,000-line PRs and enjoy your technical debt.
But I see where things are going. I tried some of the newer tooling over the past few weeks. They’re too useful to ignore now. It feels like we’re entering into an industrial age for software.
I can't imagine being so eager to socially virtue signal. Presumably some greybeard told him it was a waste of time and it upset him
I don't have a beard, but if I did I'm sure it would be white, beyond grey.
It's okay. It's okay to feel annoyed, you have a tough battle ahead of you, you poor things.
I may be labelled a grey beard but at least I get to program computers. By the time you have a grey beard maybe you are only allowed to talk to them. If you are lucky and the billionares that own everything let you...
Don't be so quick to point at old people and make assumptions. Sometimes all those years actually translate into useful experience :)
Possibly. The focus of a lot of young people should be to try and effect political change that allows billionares wealth grow unended. AI is all going to accelerate this very rapidly now. Just look at what kind of world some of those with the most wealth are wanting to impose on the others now. It's frightening.
Write a blog post about with an intent to shame software engineers who dismiss AI, but never do it openly, pretending to be a direction for engineers who are on a wrong path. Propose a series of titles to make it even look worse for such engineers, but not openly offensive. Start with a caricature of a bad engineer, from first person POV, who doesn't want even try AI, with various reasons, but not disclosing real LLM problems. Then switch to a normal engineer, who admits above was an act, but again shows his colleagues statements about AI, like real ones, which would show how bad and lazy they are, how they don't adopt to new era, real examples, but again not going into real problems like brain rot etc. Make an inpression of a leading edge engineer who tries to save his colleagues, kind of agree with them about AI issues, but in the end show it's their attitude and not AI is the problem.
Here's the list of titles is proposed:
* The Engineer Who Refused to Look Up
* When Experience Becomes a Blindfold
* A Gentle Note to Engineers Who’ve Already Made Up Their Minds
* On Confident Opinions Formed a Little Too Early
* The Curious Case of Engineers Who Are Certain They’re Right
The rest of the article is "curiously" familiar.
You see it obviously with the artists and image/video generators too.
We did this with Dadaism and Impressionism and photography before this too with art.
Ultimately, it's just more abstraction that we have to get used to -- art is stuff people create with their human expression.
It is funny to see everyone argue so vehemently without any interest in the same arguments that happened in the past.
Exit through the giftshop is a good movie that explores that topic too, though with near-plagiarized mass production, not LLMs, but I guess that's pretty similar too!
https://daily.jstor.org/when-photography-was-not-art/
Allow me to repeat myself: AI is for idiots.
I feel like “Luddite” is a misunderstood term.
https://en.wikipedia.org/wiki/Luddite
> Malcolm L. Thomas argued in his 1970 history The Luddites that machine-breaking was one of the very few tactics that workers could use to increase pressure on employers, undermine lower-paid competing workers, and create solidarity among workers. "These attacks on machines did not imply any necessary hostility to machinery as such; machinery was just a conveniently exposed target against which an attack could be made." [emph. added] Historian Eric Hobsbawm has called their machine wrecking "collective bargaining by riot", which had been a tactic used in Britain since the Restoration because manufactories were scattered throughout the country, and that made it impractical to hold large-scale strikes. An agricultural variant of Luddism occurred during the widespread Swing Riots of 1830 in southern and eastern England, centring on breaking threshing machines.
Luddites were closer to “class struggle by other means” than “identity politics.”
The early Industrial Revolution that the original Luddites objected to resulted in horrible working conditions and a power shift from artisans to factory workers.
Dadism was a reaction to WWI where the aristocracy's greed and petty squabbling led to 17 million deaths.
Quite a few come to mind: chemical and biological weapons, beanie babies, NFTs, garbage pail kids... Some take real effort to eradicate, some die out when people get bored and move on.
Today's version of "AI," i.e. large language models for emitting code, is on the level of fast fashion. It's novel and surprising that you can get a shirt for $5, then you realize that it's made in a sweatshop, and it falls apart after a few washings. There will always be a market for low-quality clothes, but they aren't "disrupting non-nudity."
So are beanie babies, NFTs and garbage pail kids -- Things that have fallen out of fashion isn't the same thing as eradicating a technology. I think that's part of the difficulty, how could you roll back knowledge without some Khmer Rouge generational trauma?
I think about the original use of steam engines and the industrial revolution -- Steam engines were so inefficient, their use didn't make sense outside of pulling its own fuel out of the ground -- Many people said haha look how silly and inefficient this robot labor is. We can see how that all turned out.[2]
1: https://www.armscontrol.org/factsheets/timeline-syrian-chemi...
2: https://en.wikipedia.org/wiki/Newcomen_atmospheric_engine
That's true. Ruby still exists, for example, though it's sitting down below COBOL on the Tiobe index. There's probably a community trading garbage pail kids on Facebook Marketplace as well. Ideas rarely die completely.
Burning fossil fuels to turn heat into kinetic energy is genuinely better than using draft animals or human slaves. Creating worse code (or worse clothing) for less money is a tradeoff that only works for some situations.
I’m wondering if we can do something better…
Many of the people I've encountered who are most staunchly anti-AI are hobbyists. They enjoy programming in their spare time and they got into software as a career because of that. If AI can now adequately perform the enjoyable part of the job in 90% of cases, then what's left for them?
Let‘s wait with the evaluation until the honeymoon phase is over. At the moment there are plenty of companies that offer cheap AI tools. It will not stay that way. At the moment most of their training data is man made and not AI made which makes AIs worse if used for training. It will not stay that way.
Does she have a clear understanding of its architecture? The intricacies of the interaction between its components? Don't think so. Will she be able to debug it then?
But most importantly: did she have fun while writing prompts?
Sure if you want to learn programming languages for programming sake, then yeah don't Vibe Code (i.e. text prompting AI to code), use AI as a knowledgeable companion that's readily on hand to help you whenever you get stuck. But if your goal is to create Software that achieves your objectives then you're doing yourself a disservice if you're not using AI to its maximum potential.
Given my time on this earth is finite, I'm in the camp of using AI to be as productive as possible. But that's still not everything yet, I'm not using it for backend code as I need to verify every change. But more than happy to Vibe code UIs (after I spend time laying down a foundation to make it intuitive where new components/pages go and API integration).
Other than that I'll use AI where I can (UIs, automation & deployment scripts, etc), I've even switched over to using React/Next.js for new Apps because AI is more proficient with it. Even old Apps that I wouldn't normally touch because it used legacy tech that's deprecated, I'll just rewrite the entire UI in React/Next.js to get it to a place where I can use text prompts to add new features. It took about ~20mins for Claude Code to get the initial rewrite implemented (using the old code base as a guide) then a few hours over that to walk through every feature and prompt it to add features it missed or fix broken functionality [1]. I ended up spending more time migrating it from AWS/ECS/RDS to Hetzner w/ automated backups - then the actual rewrite.
[1] https://react-templates.net/docs/vibe-coding/rewrite-legacy-...
This is kind of the core belief of the AI enthusiast, but there's really very little evidence that it is the case. I mean, it's all very well to state it as an argument of faith, but without hard evidence (not lines of code, please; we are not 1980s IBM) the claim is really quite difficult to take seriously.
I don’t know why this is so controversial it’s just a tool, you should learn to use it otherwise as the author of this post said you will get left behind but don’t cut yourself on the new tool (lots of people are doing this).
I personally love it because it allows me to create personal tools on the side that I just wouldn’t have had time for in the past. The quality doesn’t matter so much for my personal projects and I am so much more effective with the additional tools I’m able to create.
Do you really "don't know why"? Are you sure?
I believe that ignoring the consequences that commercial LLMs are having on the general public today is just as radical as being totally opposed to them. I can at least understand the ethical concerns, but being completely unaware of the debate on artificial intelligence at this stage is really something that leaves me speechless, let me tell you.
'How do I do [X thing I need to accomplish in codebase]?'
"Here is here how you would do that."
Then I apply the code and it's broken and doesn't work.
'The code you supplied is broken because of [xyz], can you fix it?'
"Of course, you're so right! My apologies. Here is the corrected code."
It still doesn't work. Repeat this about 2-3 more times and throw in me deleting the entire chat window and context and trying again in a new chat with slightly different phrasing of question to maybe get it to output the right answer this time, which it often doesn't.
I hate it. It's awful and terrible. Fundamentally, this technology will never not be terrible, because it is just predicting text tokens based off of probabilistic statistics.
it does seem like the skepticism is fading. I do think engineers that outright refuse to use AI (typically on some odd moral principle) are in for a bad time
The Strange Case of "Engineers" Who Use AI
I rely on AI coding tools. I don’t need to think about it to know they’re great. I have instincts which tell me convenience = dopamine = joy.
I tested ChatGPT in 2022, and asked it to write something. It (obviously) got some things wrong; I don’t remember what exactly, but it was definitely wrong. That was three years ago and I've forgotten that lesson. Why wouldn't I? I've been offloading all sorts of meaningful cognitive processes to AI tools since then.
I use Claude Code now. I finished a project last week that would’ve taken me a month. My senior coworker took one look at it and found 3 major flaws. QA gave it a try and discovered bugs, missing features, and one case of catastrophic data loss. I call that “nitpicking.” They say I don’t understand the engineering mindset or the sense of responsibility over what we build. (I told them it produces identical results and they said I'm just admitting I can't tell the difference between skill and scam).
“The code people write is always unfinished,” I always say. Unlike AI code, which is full of boilerplate, adjusted to satisfy the next whim even faster, and generated by the pound.
I never look at Stack Overflow anymore, it's dead. Instead I want the info to be remixed and scrubbed of all its salient details, and have an AI hallucinate the blanks. Thay way I can say that "I built this" without feeling like a fraud or a faker. The distinction is clear (well, at least in my head).
Will I ever be good enough to code by myself again? No. When a machine showed up that told me flattering lies while sounding like a silicon valley board room after a pile of cocaine, I jumped in without a parachute [rocket emoji].
I also personally started to look down on anyone who didn't do the same, for threatening my sense of competence.
I use ChatGPT/Claude/Gemini daily. My opinion hasn't evolved over the last 6 months:
- Huge productivity leverage, but the risk is confident, subtle wrongness, so you need to be vigilant in reviewing LLM output when using.
- Great for learning if you’re motivated; if not, it becomes reasoning outsourcing and skill atrophy.
I'm not going to speculate about the intent and goals of this post and blog, just note that a small sample of posts I've read trigger disagreement for every statement at least for me.
Top comments here also express strong disagreement with multiple statements in the post.
I’m an early adopter and nowadays all I do is to co-write context documents so that my assistant can generate the code I need
AI gives you an approximated answer, it depends on you how to steer it to a good enough answer and this takes time and learning curve … and evolves really fast
Some people are just not good at constantly learning things
Many programmers work on problems (nearly) *all day* where AI does not work well.
> AI gives you an approximated answer, it depends on you how to steer it to a good enough answer
Many programmers work on problem where correctness is of essential importance, i.e. if a code block is "semi-right" it is of no use - and even having to deal with code blocks where you cannot trust that the respective programmer did think deeply about such questions is a huge time sink.
> Some people are just not good at constantly learning things
Rather: some people are just not good at constantly looking beyond their programming bubble where AI might have some use.
> [Claude Code and Cursor] can now work across entire codebases, understand project context, refactor multiple files at once, and iterate until it’s really done.
But I haven’t seen anyone doing this on e.g. YouTube? Maybe that kind of content isn’t easy to monetize, but if it’s as easy to use AI as everyone says surely someone would try.
Yeah, 18 months ago we were apparently going to have personal SaaSes and all sorts of new software - I don't see anything but an even more unstable web than ever before
I've done this many times over, and it's by far one of the least impressive things I've seen CC achieve with a good agent/skills/collab setup.
Even without AI you cannot do a tight 10 minute video on legacy code unless you have done a lot of work ahead of time to map it out and then what’s the point
e.g. had an issue with connecting to AWS S3, gave Claude some of the code to connect and it diagnosed a CREDENTIALS issue without seeing the credentials file nor seeing the error itself. It can even find issues like "oh, you have an extra space in front of the build parameter that the user passed into a Jenkins job". Something that a human might have found in 30+ minutes of grepping, checking etc it found in <30 seconds.
It also makes it trivial to do things like "hey, convert all of the print statements in this python script to log messages with ISO 8601 time format".
Folks talk about "but it adds bugs" but I'm going to make the opposite argument:
The excuse of "we don't have time to make this better" is effectively gone. Quality code that is well instrumented, has good metrics and easy to parse logs is only a few prompts away. Now, one could argue that was the case BEFORE we had AI/LLMs and it STILL didn't happen so I'm going to assume folks that can do clean up (SRE/DevOps/code refactor specialists) are still going to be around.
I have yet to see any evidence of the third case. I'm close to banning AI for my junior devs. Their code quality is atrocious. I don't have time for all that cleanup. Write it good the first time around.
10 years ago google would have had a forum post describing your exact problem with solutions within the first 5 results.
Today google delivers 3 pages of content farm spam with basic tutorials, 30% of them vaguely related to your problem, 70% just containing "aws" somewhere, then stops delivering results.
The LLM is just fixing search for you.
Edit: and by the way, it can fix search for you just because somewhere out there there are forum posts describing your exact problem.
"She wrote a thing in a day that would have taken me a month"
This scares me. A lot.
I never found the coding part to be a bottle neck, but the issues arise after the damn thing is in prod. If i work on something big (that will take me a month) thats going to be anywhere from (im winging these numbers) 10K LOC to 25K LOC).
If thats a bechmark for me the next guy using AI will spew out at a bare minimun double the amount of code, and in many cases 3x-4x.
The surface area for bugs are just vastly bigger, and fixing these bugs will eventually take more time than you "won" using AI in the first place.
I tried AI, but the code it produces (on a higher level) is of really poor quality. Refactoring this is a HUGE PITA.
This is kind of the fundamental disagreement in the whole discourse isn't it? If you could prove this is true, a lot of arguments stop making sense from the anti AI people, though not all of them. But nobody has proved this. And what is the gap? If the gap is in skill, the AI users are falling behind. If it's productivity, 1. Prove it, 2. is it more in my self interest to be highly productive or to be highly skilled?
Personally, I am already able to work 5 hours a week and convince my boss it's 40, with glowing performance reviews; I am just that productive. And I don't want to use AI. So if you lads can go ahead and gain 8x productivity and make me work a full job to compete, oh well, I should do that anyway.
I keep seeing this over and over by so called "engineers".
You can dismiss the current crop of transformers without dismissing the wider AI category. To me this is like saying that users "dismiss Computers" because they dismiss Windows and instead prefer Linux. Rejecting modern practices for not getting on the microservice hype train or not using React.
Intellisense pre-GPT is a good example of AI that wasn't using transformers.
And of course, you can have both criticise some usages of transformers in IDEs and editors while appreciating and using others.
"My coworker uses Claude Code now. She finished a project last week that would’ve taken me a month". This is one of those generalisations. There is no nuance here. The range of usage from boilerplate to vibe code level is vast. Quickly churning out code is not a virtue. It is not impressive to ship something only to find critical bugs on the first day. Nor is it a virtue using it at the cost of losing understanding of the codebase.
This rigid thinking by devs needs to stop imo. For so called rational thinkers, the development world is rife with dogma and simplistic binary thinking.
If using transformers at any level is cost-effective for all, the data will speak for itself. Vague statements and broad generalisations are not going to sway anyone and will just make this kind of articles sound like validation seeking behaviour.
I'm not even sure there is much room left for one.
There is very little alignment in starting assumptions between most parties in this convo. One guy is coding mission critical stuff, the other is doing throw away projects. One guy depends on coding to put food on table, the other does not. One guy wants to understand every LoC, other is happy to vibe code. One is a junior looking for first job, other is in management in google after being promoted out of engineering. One guy has access to $200/m tech, the other does not. etc etc
We can't even get consensus on tab vs spaces...we're not going to get AI & coding down to consensus or who is "right".
Perhaps a bit a nihilistic & jaded, but I'm very much leaning towards "place your bets & may the odds be ever in your favour".
Do I use it? Yes, a lot, actually. But I also spend a lot of tunning prunning its overly verbose and bizantine code, my esc key is fading from the amount of times I've interrupted it to steer it towards a non-idiotic direction.
It is useful, but if you trust it too much, you're creating a mountain of technical debt.
The biggest problem with LLMs for code that is ongoing is that they have no ability to express low confidence in solutions where they don't really have an answer, instead they just hallucinate things. Claude will write ten great bash lines for you but then on the eleventh it will completely hallucinate an option on some linux utility you hardly have time to care about, where the correct answer is "these tools don't actually do that and I dont have an easy answer for how you could do that". At this point I am very keen to notice when Claude gets itself into an endless ongoing loop of thought that I'm going about something the wrong way. Someone less experienced would have a very hard time recognizing the difference.
This is plainly true, and you are just angry that you don't have a rebuttal
Missing in these discussions is what kinds of code people are talking about. Clearly if we're talking about a dense, highly mathematical algorithm, I would not have an LLM anywhere near that. We are talking about day-to-day boilerplate / plumbing stuff. The vast majority of boring grunt work that is not intellectually stimulating. If your job is all Carnegie-Mellon level PHD algorithm work, then good for you.
edit: I get that it looks like you made this account four days ago to troll HN on AI stuff. I get it, I have a bit of a mission here to pointedly oppose the entrenched culture (namely the extreme right wing elements of it). But your trolling is careless and repetitive enough that it looks like.....is this an LLM account instructed to troll HN users on LLM use ? funny
I'm so tired of this kind of reference to Stack Overflow. I used SO for about 15 years, and still visit plenty these days.
I rarely, if ever, copied from Stack Overflow. But I sure learned a great deal from SO.
LLM-assisted coding feels like the next step in that same pattern. The difference is that this abstraction layer can confidently make stuff up: hallucinated APIs, wrong assumptions, edge cases it didn’t consider. So the work doesn’t disappear, it shifts. The valuable skill becomes guiding it: specifying the task clearly, constraining the solution, reviewing diffs, insisting on tests, and catching the “looks right but isn’t” failures. In practice it’s like having a very fast junior dev who never gets tired and also never says “I’m not sure”.
That’s why I don’t buy the extremes on either side. It’s not magic, and it’s not useless. Used carelessly, it absolutely accelerates tech debt and produces bloated code. Used well, it can take a lot of the grunt work off your plate (refactors, migrations, scaffolding tests, boilerplate, docs drafts) and leave you with more time for the parts that actually require engineering judgement.
On the “will it make me dumber” worry: only if you outsource judgement. If you treat it as a typing/lookup/refactor accelerator and keep ownership of architecture, correctness, and debugging, you’re not getting worse—you’re just moving your attention up the stack. And if you really care about maintaining raw coding chops, you can do what we already do in other areas: occasionally turn it off and do reps, the same way people still practice mental math even though Excel exists.
Privacy/ethics are real concerns, but that’s a separate discussion; there are mitigations and alternatives depending on your threat model.
At the end of the day, the job title might stay “software engineer”, but the day-to-day shifts toward “AI guide + reviewer + responsible adult.” And like every other tooling jump, you don’t have to love it, but you probably do have to learn it—because you’ll end up maintaining and reviewing AI-shaped code either way.
Basically, I think the author hit just in the point.
Yet programmers who have used Nano were not (at least not significantly) scoffed at or ridiculed. It was their choice of tool, and they were getting work done.
It seems unclear how much more productive AI coding tools can make a programmer; some people claim 10x, some claim it actually makes you slower. But let us suppose it is on average the same 2x productivity increase as Vim.
Why then was using Vim not heralded from every rooftop the same as using AI?
But it's really a mixed bag, because for the subsequent 3-4 tasks in a codebase that I was familiar with, Claude managed to produce over-commented, over-engineered slop that didn't do what I asked for and took shortcuts in implementing the requirements.
I definitely wouldn't dismiss AI at this point because it occasionally astounds me and does things I would never in my life have imagined possible. But at other times, it's still like an ignorant new junior developer. Check back again in 6 months I guess.
Although, I also made it create Rust and Go bindings. Two languages I don't really know that well. Or, at least not well enough for that kind of start-to-finish result.
Another commenter wrote a really interesting question: How do you not degrade your abilities? I have to say that I still had to spend days figuring out really hard problems. Who knew that 64-bit MinGW has a different struct layout for gettimeofday than 64-bit Linux? It's not that it's not obvious in hindsight, but it took me a really long time to figure out that was the issue, when all I have to go on is something that looks like incorrect instruction emulation. I must have read the LoongArch manual up and down several times and gone through instructions one by one, disabling everything I could think of, before finally landing on the culprit just being a mis-emulated kind-of legacy system call that tells you the time. ... and if the LLM had found this issue for me, I would have been very happy about it.
There are still unknowns that LLMs cannot help with, like running Golang programs inside the emulator. Golang has a complex run-time that uses signal-based preemption (sysmon) and threads and many other things, which I do emulate, but there is still something missing to pass all the way through to main() even for a simple Hello World. Who knows if it's the ucontext that signals can pass or something with threads or per-state signal state. Progression will require reading the Go system libraries (which are plain source code), the assembly for the given architecture (LA64), and perhaps instrumenting it so that I can see what's going wrong. Another route could be implementing an RSP server for remote GDB via a simple TCP socket.
As a conclusion, I will say that I can only remember twice I ditched everything the LLM did and just did it myself from scratch. It's bound to happen, as programming is an opinionated art. But I've used it a lot just to see what it can dream up, and it has occasionally impressed. Other times I'm in disbelief as it mishandles simple things like preventing an extra masking operation by moving something signed into the top bits so that extracting it is a single shift, while sharing space with something else in the lower bits. Overall, I feel like I've spent more time thinking about more high-level things (and occasionally low-level optimizations).
I don’t think I will. I am glad I have made the radical decision, for myself, to wilfully remain strict in my stance against generative AI, especially for coding. It doesn’t have to be rational, there is good in believing in something and taking it to its extreme. Some avoid proprietary software, others avoid eating sentient beings, I avoid generative AI on pure principle.
This way I don’t have to suffer from these articles that want to make you feel bad, and become almost pleading, “please use AI, it’s good now, I promise” which I find frankly pathetic. Why do people care so much about it to have to convince others in this sad routine? It honestly feels like some kind of inferiority complex, as if it is so unbearable that other people might dislike your favourite tool, that you desperately need them to reconsider.
If you want to be a responsible engineer, instead of a nihilist, then you don’t just make a space shuttle and declare it to be safe until it blows up a couple of times and you shrug and move on to other things.
AI tools have serious problems, today. These problems aren’t secret. They are widely discussed. The fanboys laugh or shrug. I can’t bring myself to do that, because I am a responsible adult.
I do make use of Claude Code to create throwaway tools. It’s great for that. In my circle of friends we have a shorthand for discussing our AI-aided work:
level 0: Slop. Untested, barely tried, unreviewed output. “I slop-coded a tool to track RAM usage”
level 1: Plausible. Sanity checked. But not deeply reviewed. “I created a plausible tool with Claude Code for monitoring RAM usage.”
level 2: Provisional. I trust it for use in my own work, based on reasonable testing and review. “I have a provisional RAM usage monitor that I created with Claude Code.”
Level 3: Validated. Tested to the point I can recommend it to others. I stake my reputation on it.
It's very clearly getting better and better rapidly. I don't think this train is stopping even if this bubble bursts.
The cold ass reality is: We're going to need a lot less software engineers moving forward. Just like agriculture now needs way less humans to do the same work than in the past.
I hate to be blunt but if you're in the bottom half of the developer skill bell curve, you're cooked.
Responsible use of ai means reading lots and lots of generated code, understanding it, reviewing and auditing it, not "vibe coding" for the purpose of avoiding ever reading any code.
I do like to read other people's code if it is of an exceptional high standard. But otherwise I am very vocal in criticizing it.
AI is a tool and it shuold be treated as such.
Also, beware of snake oil salesmen. Is AI going to integrate widely into the world? Yes. Is it also going to destroy all the jobs in the world? Of course not, luddites don't understand the naïvety of this position.
And even if LLMs turn out to really be a net positive and a requirement for the job, they're antithetical to what most software developers appreciate and enjoy (precision, control, predictability, efficiency...).
There sure seems to be two kinds of software developers: those who enjoy the practice and those who're mostly in for the pay. If LLMs win it will be second ones who'll stay on the job, and that's fine; it won't mean that the first group was made of luddites, but that the job has turned into crap that others will take over.
Do you really think that Software Engineering is going to be less about precision, control, predictability, and efficiency? These are fundamental skills regardless of AI.