The hidden cost of AI coding

You likely don’t have a say in the matter, but you should have a junior developer. That’s where senior developers come from.

Where do you draw that line though?

If you're talking about a person using an LLM, or some other ML system, to help generate their music then the LLM is really just a tool for that person.

I can't run 80 mph but I can drive a car that fast, its my tool to get the job done. Should I not be allowed to do that professionally if I'm not actually the one achieving that speed or carrying capacity?

Personally my concerns with LLMs are more related to the unintended consequences and all the unknowns in play given that we don't really know how they work and aren't spending much effort solving interoperability. If they only ever end up being a tool, that seems a lot more in line with previous technological advancements.

Why?

What has always held true so far: <new tool x> abstracts challenging parts of a task away. The only people you will outcompete are those, who now add little over <new tool x>.

But: If in the future people are just using <new tool x> to create a product that a lot of people can easily produce with <new tool x>, then, before long, that's not enough to stand out anymore. The floor has risen and the only way to stand out will always be to use <new tool x> in a way that other people don't.

People who can't spin pottery shouldn't be allowed to have bowls, especially mass produced by machine ones.

I understand your point, but I think it is ultimately rooted in a romantic view of the world, rather than the practical truth we live in. We all live a life completely inundated with things we have no expertise in, available to us at almost trivial cost. In fact it is so prevalent that just about everyone takes it for granted.

Your company doesn’t care about how you got to the end, they just care about did you get there and meet all of the functional and non functional requirements.

My entire management chain - manager, director and CTO - are all technical and my CTO was a senior dev at BigTech less then two years ago. But when I have a conversation with any of them, they mostly care about whether the project I’m working on/leading is done on time/within budget/meets requirements.

As long as those three goals are met, money appears in my account.

One of the most renown producers in hip hop - Dr. Dre - made a career in reusing old melodies. Are (were) his protégés - Easy-E, Tupac, Snoop, Eminem, 50 cent, Kendrick Lamar, etc - not real musicians?

> So if someone generates their music with AI to get their idea to music you don’t look down on it?

It depends entirely on how they're using it. AI is a tool, and it can be used to help produce some wonderful things.

- I don't look down on a photographer because they use a tool to take a beautiful picture (that would have taken a painter longer to paint)

- I don't look down on someone using digital art tools to blur/blend/manipulate their work in interesting ways

- I don't look down on musicians that feed their output through a board to change the way it sounds

AI (and lots of other tools) can be used to replace the creative process, which is not great. But it can also be used to enhance the creative process, which _is_ great.

If they used an algorithm to come up with a cool melody and then did something with it, why look down on it?

Look at popular music for the last 400 years. How is that any different than simply copying the previous generations stuff and putting your own spin on it?

If you heard a CD in 1986 then in 2015 you wrote a song subconsciously inspired by that tune, should I look down on you?

I mean, I'm not a huge fan of electronic music because the vast majority of it sounds the same to me, but I don't argue that they are not "real musicians".

I do think that some genres of music will age better than others, but that's a totally different topic.

> I've always distilled this down to people who like the "craft" and those who like the "result".

I find it very unlikely anyone who only likes the results will ever pick up the craft in the first place

It takes a very specific sort of person to push through learning a craft they dislike (or don't care about) just because they want a result badly enough

What's "the result"? Because I don't like how this divide is being stated (it's pretty common).

Seems to me that "the result" is "the money" and not "the product".

Because I'd argue those that care about the product, the thing being built, the tool, take a lot of pride in their work. They don't cut corners. They'll slog through the tough stuff to get things done.

These things align much more with the "loves coding" group than "the result". Frankly, everyone cares about "the result" and I think we should be clear about what is actually meant

> like the laws of physics that influence the cord patterns we use in music.

So much of what we think of as law in music is just being used to the conventions. Lots of amazing music would have been considered noise if created in an earlier time.

> The issue with programming is that it isn't like music or really any other skill where you get feedback right away and operate in a well understood environment.

Funny, I feel the opposite about programming. The feedback comes in milliseconds. Ok the build didn’t break, ok the ui is working, now check if the change I made is right, now run the tests, etc. and the environment is fully documented, depending on your tooling of choice and the quality of its docs.

> What I've noticed in that respect is that I just read what it does and then immediately reason why it's there ....

How if it hallucinate and gives you wrong code and explanation? It is better to read documentations and tutorials first.

> The AI takes care of the tedious line by line what’s-the-name-of-that-stdlib-function parts (and most of the tedious test-writing parts)

AI generated tests are a bad idea.

Aren't stdlib functions the ones you know by heart after a while anyways?

Yes, there are bad places to work. There are also places that require detailed time tracking, do not allow any time to write tests, have very long hours, tons of on-call alerts, etc.

And even when that's not the case you are still indirectly working with them because your coworker is and "somehow" their code has gotten worse

No, because it's usually a few years old and already obsolete - the frameworks and the language have gone through a gazillion changes and what you did in 2021 suddenly no longer works at all.

I keep seeing that suggestion as well and the only sensible way I see would be to use one off boilerplate, anything else does not make sense.

If you keep re-using boilerplate once in a while copying it from elsewhere is fine. If you re-use it all the time, just get a macro setup in your editor of choice. IMHO that is way more efficient than asking AI to produce somewhat consistent boilerplate

Custodians of human knowledge.

That’s only true if you tokenize words rather than characters. Character tokenization generates new content outside the training vocabulary.

Not even close, those were all deterministic, this is probabilistic.

I already see this happening with low code, SaaS and MACH architectures.

What used to be a project doing a CMS backend, now is spent doing configurations on a SaaS product, and if we are lucky, a few containers/serveless for integrations.

There are already AI based products that can automate those integrations if given enough data samples.

Many believe AI will keep using current programming languages as translation step, just like those Assembly developers thought compiling via Assembly text generation and feeding into an Assembly would still be around.

"There are a thousand other ways of solving problems for real people, so that doesn't explain why some choose software development as their preferred method."

Software development is almost unique in the scale that it operates at. I can write code once and have it solve problems for dozens, hundreds, thousands or even millions of people.

If you want your work to solve problems for large numbers of people I have trouble thinking of any other form of work that's this accessible but allows you to help this many others.

Fields like civil engineering are a lot harder to break into!

> That would pretty much be a life crisis, as I'd have to find and train for something else.

There's inertia in the industry. It's not like what you're describing could happen in the blink of an eye. You may well be at the end of your career when this prophecy is fulfilled, if it ever comes true. I sure will be at the end of mine and I'll probably work for at least another 20 years.

> No. There are a thousand other ways of solving problems for real people, so that doesn't explain why some choose software development as their preferred method.

I don't see why we should seek an explanation if there are thousands of ways to be useful to people. Is being a lawyer particularly better than being an accountant?

I'm probably just not as smart or creative as you, but say my problem is I have a ski cabin that I want to rent it to strangers for money. Nevermind a thousand, What are 100 ways without using software that I could do something about that, vs listing it on Airbnb?

Those of us who write software professionally are literally in a field premised on automating other people's jobs away. There is no profession with less claim to the moral high ground of worker rights than ours.

That's been my experience. If it's been solved a million times, it's helpful. If you're out on the frontier where there's no public code, it's worse than useless.

If you're somewhere in between (where I am now) it's situationally useful for small sub-components but you need to filter it heavily or you'll end up wasting a day or two going down a wrong rabbit-hole either because you don't know the domain well enough to tell when it's bullshitting or going down a wrong path, or don't know the domain well enough to use the right keyword to get it to cough up something useful. I've found domain knowledge essential for deciding when it's doing something obviously wrong instead of saying "I don't know" or "This is the wrong approach to the problem".

For the correct self-contained class or block of code, it is much faster to specify the requirements and go through a round or two of refinement than it is to write it myself. For the wrong block of code it's a complete waste of time. I've experienced both in the last few days.

For me it's been toy games built on web languages, which happens to be something I toyed with via my actual raw skills for the past 15 years. LLMs have opened many new doors and options for what I can build because I now technically "know everything" in the world via LLMs. Stuff that I would get stuck wasting hours on is now solved in minutes. But then it ALWAYS reaches a point where the complexity the LLM has generated is too much and the model can no longer iterate on what it's built.

The issue with "AIOP" is that you don't have a litany of others (as is the case with SO) providing counter examples, opinions, updated best practices, etc. People take the AI output as gospel and suffer for it without being exposed so the ambiguity that surrounds implementing things.

Will an engineering team ever be able to craft a thing of wonder, that surprises and delights? I think great software can do that. But I've seen it arise only rarely, and almost always as originating from one enlightened mind, someone who imagined a better way than the well-trod paths taken by so many who went before. I can imagine AI as a means to go only 'where man gas gone before'.

I've heard a _lot_ of stories like this. What I haven't heard is stories about the deployment of said applications and the ability of the human-side author to maintain the application. I guess that's because we're in early days for LLM coding, or the people who did this aren't talking (about their presumed failures... people tend to talk about successes publicly, not the failures).

Only 150 hours of programming in 15 years? Are you in more of an Architect / Tech Lead role than an IC (individual contributor) role?

I think there are a lot of us, but the people who dislike AI are much more vocal in online conversations about it.

(The hype merchant, LinkedIn influencer, Twitter thread crowd are super noisy but tend to stick to their own echo chambers, it's rare to have them engage in a forum like Hacker News directly.)

We're the silent majority, I'm pretty sure. If you love coding, you probably love technology, and if you love technology, you probably love AI, which is inarguably the most interesting tech advancement in this decade.

The others, who are not like us? They've got other priorities. If you hate coding but you love AI, you're probably into software engineering because of the money, not love of technology. If you love coding and you hate AI, you're probably more committed to some sort of ideology than you are the love of technology. If you hate coding and you hate AI, well, I hope you throw your cellphone into the river and find a nice cabin in the woods somewhere to hide in.

> I don't know if I'm a minority

No, there's plenty of top-class engineers who love coding with AI. e.g. Antirez.

I love AI as a concept.

I hate the reality of our current AI, which is benefitting corporations over workers, being used for surveillance and censorship (nevermind direct social control via misinformation bots), and is copying the work of millions without compensating them in order to do it.

And the push for coders to use it to increase their output, will likely just end up meaning expectations of more LoC and more features faster, for the same pay.

But FOSS, self-hosted LLMs? Awesome!

First of all, Lisp, Fortran and COBOL had been around most of the time when assembly was popular. Assembly was used because of resource constraints.

Secondly, you are not writing anything you get from an LLM. You prompt it and it spits out other people's code, stripped of attribution.

This is what children do: Ask someone to fix something for you without understanding the result.

It’s almost like there’s a big range of different comprehension styles among human beings, and a varying set of preferences that go with those.

It is absolutely possible to enjoy both- I have used LLMs to generate code for ideas about alternate paths to take when I write my code- but prompt generation is not coding, and there are WAY too many people who claim to be coding when they have in fact done nothing of the sort.

> a far higher intensity

I'm not sure what this is supposed to mean. The code that I've gotten is riddled with mistakes and fabrications. If I were to use it directly, it would significantly slow my pace. Likewise, when I use LLMs to offer alternative methods to accomplish something, I have to take the time to sit down and understand what they're proposing, how to actually make it work, and whether that route(s) would be better than my original idea. That is a significant speed reduction.

The only way I can imagine LLMs resulting in "far higher intensity" is if I was just yolo'ing the code into my program, and then doing frantic integration, correction, and bugfix work afterwards.

Sure, that's "higher intensity", but that's just working harder and not smarter.

It is not coding the same way riding a bus is not driving

You may get to the same destination, but it is not the same activity

If you create a sufficiently absurd hypothetical, anything is possible.

Or are you calling an LLM a "clone" of you? In that case, it's more, "if you create a flawed enough starting premise, anything is possible".

Who write boilerplate this day? I just lift the code from the examples in the docs (especially css frameworks). And I love looking at functions docs, because after doing it a few times, you develop an holistic understanding of the library and your speed increases. Kinda like learning a foreign language. You can use an app to translate everything, or asks for the correct word when the needs arises. The latter is a bit frustrating at the beginning, but that’s the only way to become fluent.

I think the analogy/ substitution falls apart in that singing is generally not very stable or lucrative (for 99.999% of singers), so it is pretty rare to find someone singing who hates it. Much less uncommon to find people working in IT who hate the specific work of their jobs.

And I think we do tend to (rightfully) look down on e.g. singers who lip-sync concerts or use autotune to sing at pitches they otherwise can't, nevermind how we'd react if one used AI singing instead of themselves.

Yes, loving something is no guarantee of skill at it, but hating something is very likely to correspond to not being good at it, since skills take time and dedication to hone. Being bad at something is the default state.

I love coding - but I am not very good at it. I can describe what I want in great detail, with great specificity. But I am not personally very good at turning that detailed specification into the proper syntax and incantations.

AI is like jet fuel for me. It’s the translation layer between specs and code I’ve always wanted. It’s a great advisor for implementation strategies. It’s a way to try new strategies in code quickly.

I don’t need to get anyone else to review my code. Most of this is for personal projects.

I don’t really write professionally, so I don’t have a ton of need for it to manage realities of software engineering (large codebases, peer reviews, black box internal systems, etc). That being said - I do a reasonable amount of embedded Linux work, and AI understands the Linux kernel and device drivers very well.

To extend your metaphor: AI is like a magic microphone that makes all of my singing sound like Tony Rice, my personal favorite singer. I’ve always wanted to sound like him - but I never will. I don’t have the range or the training. But AI allows my coding level to get to that corresponding level with writing software.

I absolutely love it.

> And while it is easier to become good at coding than at singing - for professional purposes at least - I believe that the effect still holds.

I can't reconcile this with my own view of things but I think "for professional purposes at least" is doing a lot of work in your sentence and I get the feeling you intend to say "good enough" by adding that bit.

Most programmers are very bad at programming (and problem solving) and only if they were compared to absolute beginners with zero insight could they be said to be "good" (and most of that comes down to them at least knowing the names of maybe a couple of concepts, etc., which makes for at least a partial map of the knowledge space). Most of them will never become good at what they do either, but will stay middling because they basically just glue libraries together and learn surface level things over and over.

If all you've ever done is learn a language, a backend framework in that language, learn how to use SQL, learn JavaScript, learn a couple of frontend frameworks for JavaScript, you've just basically learned a bunch of trivia that at best could be considered table stakes for a certain part of the industry.

If you're not actually doing free form problem solving, implementing things from scratch, reading code you didn't have to read and building and reinforcing your own fundamentals in other ways you won't ever be a good programmer.

I've worked with people who've spent 10+ years in the industry only to be essentially useless without frameworks and libraries and it regularly showed in how poor their output was. It wasn't any better when they did have frameworks and libraries to use, but they could pretend it was because at least a solution was reached. The truth is that in most of those cases a much better version could've been reached by simply re-implementing only the parts that were needed, but these types of programmers don't have the capability to do so.

That comment makes me deeply suspicious about your debugging skills. And the formatting of your code.

This doesn't even make sense, forgetting a semicolon is immediately caught by the compiler. What positive benefits does AI provide here?

Yes, I have the same feeling about the wall/hype cycle. Most of my time is understanding code and formulating a plan to change code w/o breaking anything... even if LLMs would generate 100% perfect code on the first try, it would not help in a big way.

One thing I forgot to mention is asking LLMs questions from within the IDE instead of doing a web search... this works quite nice, but again, it is not a crazy productivity boost.

Jetbrains AI runs on a "discount LLM" and their ratings were below 2 stars. I tried two others, which played games with me to reduce context and use cheaper models. I then switched to Aider which leads me to believe a moderate Claude user may need to spend 30$ a month, but I use Gemini models and I didnt exceed 5$.

I've had the same experience as the person to whom you're responding. After reading your post, I have to ask: if you're putting so much effort into prompting it with specific points, correcting it often, etc., why not just write the code yourself? It sounds like you're putting a good deal of effort into prompting it.

Aren't you worried that overtime you'll rely on it too much and your offhand knowledge will get worse?

I do not rule out, that I am just very bad with prompting.

It just surprises me, that you write you had massive successes with "java, js/TS, html css, go, rust, python, bitbucket pipelines/GitHub actions, cdk, docker compose, SQL, flutter/dart, swift etc.", if you include the usual libraries/frameworks and the diverse application areas for these technologies, even with LLMs support it seems to me crazy to be able to make meaningful contributions in non trivial code bases.

Concerning SQL I can report another fail with LLMs, in a trivial code base with a handful of entities the LLM cannot come up with basic window functions.

I would be very interested if you could write up a blog post or could make a youtube video demonstrating your prompting skills... Perhaps demonstrating with a bigger open source project in any of the mentioned languages how to add a non trivial feature with your prompting skills?

> You are just bad with prompting or working with very obscure language/framework or bad coding pattern or all of it

You just described every existing legacy project^^

Could you perhaps point me to a youtube video which demonstrates an experienced prompter sculpting code with Cursor/Clause Code?

In my search I just found trivial examples.

My critic so far:

- Examples seem always to be creating a simple application from scratch

- Examples always use super common things (like create a blog / simple website for CRUD)

What I would love to see (see elsewhere): Adding a non trivial feature to a bigger code base. Just a youtube video/demonstration. I don't care about language/framework etc. ...

Copilot can also use Gemini 2.5 and sonnet 3.7.

Fair enough. Still, I was out of luck for some fairly simple SQL statements, were the model knows 100% of the DDL statements.

We used to make fun of and look down on coders who mindlessly copy paste and mash the compile button until the code runs, for good reasons.

Then don't use it? Nobody is making you.

I am also barely using LLMs at the moment. Even 10% of the time would be generous.

What I was saying is that I have tried different ways of interacting with LLMs and was happy to discover that the way I describe stuff to another senior dev actually works quite fine with an LLM. So I stuck to that.

Again, if an LLM is not up to your task, don't waste your time with it. I am not advocating for "forget everything you knew and just go ask Mr. AI". I am advocating for enabling and productivity-boosting. Some tasks I hate, for some I lack the deeper expertise, others are just verbose and require a ton of typing. If you can prompt the LLM well and vet the code yourself after (something many commenters here deliberately omit so they can happily tear down their straw man) then the LLM will be a net positive.

It's one more tool in the box. That's all there is to it really. No idea why people get so polarizing.

Prompt engineering is just trying that task on a variety of models and prompt variations until you can better understand the syntax needed to get the desired outcome, if the desired outcome can be gotten.

Honestly you’re trying to prove AI is ineffective by telling us it didn’t work with your ineffective protocol. That is not a strong argument.

Nice. I've got a whole lot of magical things that I need built for my day job. Want to connect so I can hand the work over to you? I'll still collect the paychecks, but you can have the joy. :)

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> I don’t think this is the case, if anything the opposite is true

I'll be a radical and say that I think it depends and is very subjective.

Author above you seems to enjoy working on code by itself. You seem to have a different motivation. My motivation is solving problems I encounter, code just happen to be one way out of many possible ones. The author of the submission article seems to love the craft of programming in itself, maybe the problem itself doesn't even matter. Some people program just for the money, and so on.

Well, does not help that a lot of work tasks are meaningless drudgery that we collectively should have trivialized and 100% automated at least 20 years. That was kind of the core my point: a lot of work tasks are just plain BS.

We all delegate. Did you knit your own clothes or is that too boring for you?

Enjoying to code/knit is fine but we can no longer expect to get paid well to do it.

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1. I work on enjoyable problems after I let the LLM do some of the tasks I have to do for money. The LLM frees me bandwidth for the stuff I truly love. I adore solving problems with code and that's not going to change ever.

2. Some of the modern LLMs generate very impressive code. Variables caching values that are reused several times, utility functions, even closure helpers scoped to a single function. I agree that when the LLM code's quality falls bellow a certain threshold then it's better in every way to just write it yourself instead.

A super necessary context here is that I barely use LLM at all still. Maybe I should have said so but I figured that too much nuance would ruin a top-level comment and mostly casually commented on a tradeoff of using or not using LLMs.

Where I use LLMs:

1. Super boring and annoying tasks. Yes, my prompts for those include various coding style instructions, requests for small clarifying comments where the goal of the code is not obvious, tests. So, no OSS license restrictions. Libraries I specify most of the times I used LLMs (and only once did I ask it to suggest a library). Logging and telemetry I add myself. So long story short, I use the LLM to show me a draft of a solution and then mercilessly refactor it to match my practices and guidelines. I don't do 50 exchanges out of laziness, no.

2. Tasks where my expertise is lacking. I recently used an LLM to help me with making a `.clone()`-heavy Rust code to become nearly zero-copy for performance reasons -- it is a code on a hot path. As much as I love Rust and I am fairly good at it (realistically I'm IMO at 7.5 / 10), all the lifetimes and zero-copy semantics I still don't know yet. A long session with an LLM after, I emerged both better educated and with a faster code. IMO a win-win.

Exactly, how are you supposed to verify anything when you don't have any skills left beyond prompting.

Absolutely. That's why I don't give the LLM the reins for long, nor do I tell it to do the whole thing. I want to keep my mind sharp and my abilities honed.

Companies pay with money for specific features they want, just like individuals pay with their even more valuable time to add features they want.

This is fine, so long as the community decides what features they actually want to create the "menu" of unfunded objectives donors can sponsor