Users typically don't read code, developers (of the software) do.

If it's not worth reading something where the writer didn't take the time to write it, by extension that means nobody read the code.

Which means nobody understands it, beyond the external behaviour they've tested.

I'd have some issues with using such software, at least where reliability matters. Blackbox testing only gets you so far.

But I guess as opposed to other types of writing, developers _do_ read generated code. At least as soon as something goes wrong.

> even if no one reads it

I gotta disagree with you there! Code that isn't read doesn't do anything. Code must be read to be compiled, it must be read to be interpreted, etc.

I think this points to a difference in our understanding of "read" means, perhaps? To expand my pithy "not gonna read if you didn't write" bit: The idea that code stands on its own is a lie. The world changes around code and code must be changed to keep up with the world. Every "program" (is the git I run the same as the git you run?) is a living document that people maintain as need be. So when we extend the "not read / didn't write" it's not using the program (which I guess is like taking the lessons from a book) it's maintaining the program.

So I think it's possible that I could derive benefit from someone else reading an llm's text output (they get an idea) - but what we are trying to talk about is the work of maintaining a text.

It makes sense. A vibe-coded tool can sometimes do the job, just like some cheap Chinese-made widget. Not every task requires hand-crafted professional grade tools.

For example, I have a few letter generators on my website. The letters are often verified by a lawyer, but the generator could totally be vibe-coded. It's basically an HTML form that fills in the blanks in the template. Other tools are basically "take input, run calculation, show output". If I can plug in a well-tested calculation, AI could easily build the rest of the tool. I have been staunchly against using AI in my line of work, but this is an acceptable use of it.

>Code has a pretty important property that ordinary prose doesn’t have

But isn't this the distinction that language models are collapsing? There are 'prose' prompt collections that certainly make (programmatic) things happen, just as there is significant concern about the effect of LLM-generated prose on social media, influence campaigns, etc.

> it can make real things happen even if no one reads it (it’s executable).

"One" is the operative word here, supposing this includes only humans and excludes AI agents. When code is executed, it does get read (by the computer). Making that happen is a conscious choice on the part of a human operator.

The same kind of conscious choice can feed writing to an LLM to see what it does in response. That is much the same kind of "execution", just non-deterministic (and, when given any tools beyond standard input and standard output, potentially dangerous in all the same ways, but worse because of the nondeterminism).

Sometimes (or often) things with horrible security flaws "work" but not in the way that they should and are exposing you to risk.

I guess it depends on whether you're only executing the code or if you're submitting it for humans to review. If your use case is so low-stakes that a review isn't required, then vibe coding is much more defensible. But if code quality matters even slightly, such that you need to review the code, then you run into the same problems that you do with AI-generated prose: nobody wants to read what you couldn't be bothered to write.

> But I’ll gladly use a tool someone had an AI write, as long as it works (which these things increasingly do).

It works, sure, but is it worth your time to use? I think a common blind spot for software engineers is understanding how hard it is to get people to use software they aren’t effectively forced to use (through work or in order to gain access to something or ‘network effects’ or whatever).

Most people’s time and attention is precious, their habits are ingrained, and they are fundamentally pretty lazy.

And people that don’t fall into the ‘most people’ I just described, probably won’t want to use software you had an LLM write up when they could have just done it themselves to meet their exact need. UNLESS it’s something very novel that came from a bit of innovation that LLMs are incapable of. But that bit isn’t what we are talking about here, I don’t think.

Hell, I'd read an instruction manual that AI wrote as long as it accurately describes.

I see a lot of these discussions where a person gets feelings/feels mad about something and suddenly a lot of black and white thinking starts happening. I guess that's just part of being human.

>but many tools I use daily are closed source

I wonder if this is a major differentiator between AI fans and detractors. I dislike and actively avoid anything closed source. I fully agree with the premise of the submission as well.

I agree with your sentiment, and it touches on one of the reasons I left academia for IT. Scientific research is preoccupied with finding the truth, which is beautiful but very stressful. If you're a perfectionist, you're always questioning yourself: "Did I actually find something meaningful, or is it just noise? Did I gaslight myself into thinking I was just exploring the data when I was actually p-hacking the results?" This took a real toll on my mental health.

Although I love science, I'm much happier building programs. "Does the program do what the client expects with reasonable performance and safety? Yes? Ship it."

similarly, i think that something that someone took the time to proof-read/verify can be of value, even if they did not directly write it.

this is the literary equivalent of compiling and running the code.

> I only care if it works

Okay but it is probably not going to be a tool that will be reliable or work as expected for too long depending on how complex it is, how easily it can be understood, and how it can handle updates to libraries, etc. that it is using.

Also, what is our trust with this “tool”? E.g. this is to be used in a brain surgery that you’ll undergo, would you still be fine with using something generated by AI?

Earlier you couldn’t even read something it generated, but we’ll trust a “tool” it created because we believe it works? Why do we believe it will work? Because a computer created it? That’s our own bias towards computing that we assume that it is impartial but this is a probabilistic model trained on data that is just as biased as we are.

I cannot imagine that you have not witnessed these models creating false information that you were able to identify. Understanding their failure on basic understandings, how then could we trust it with engineering tasks? Just because “it works”? What does that mean and how can we be certain? QA perhaps but ask any engineer here if companies are giving a single shit about QA while they’re making them shove out so much slop, and the answer is going to be disappointing.

I don’t think we should trust these things even if we’re not developers. There isn’t anyone to hold accountable if (and when) things go wrong with their outputs.

All I have seen AI be extremely good at is deceiving people, and that is my true concern with generative technologies. Then I must ask, if we know that its only effective use case is deception, why then should I trust ANY tool it created?

Maybe the stakes are quite low, maybe it is just a video player that you use to watch your Sword and Sandal flicks. Ok sure, but maybe someone uses that same video player for an exoscope and the data it is presenting to your neurosurgeon is incorrect causing them to perform an action they otherwise would have not done if provided with the correct information.

We should not be so laissez-faire with this technology.

Honest conversation in the AI era is just sending your prompts straight to each other.

It's bad enough they didn't bother to actually write it, but often it seems like they also didn't bother to read it either.

This is the dark comedy of the AI communication era — two LLMs having a conversation with each other while their human operators have already checked out. The email equivalent of two answering machines leaving messages for each other in the 90s.

The real cost isn't the tokens, it's the attention debt. Every CC'd person now has to triage whether any of those paragraphs contain an actual decision or action item. In my experience running multiple products, the signal-to-noise ratio in AI-drafted comms is brutal. The text looks professional, reads smoothly, but says almost nothing.

I've started treating any email over ~4 paragraphs the same way I treat Terms of Service — skim the first sentence of each paragraph and hope nothing important is buried in paragraph seven.

In other words, AI partially solves the technique problem, but not the taste problem.

In the arts the differentiators have always been technical skill, technical inventiveness, original imagination, and taste - the indefinable factor that makes one creative work more resonant than another.

AI automates some of those, often to a better-than-median extent. But so far taste remains elusive. It's the opposite of the "Throw everything in a bucket and fish out some interesting interpolation of it by poking around with some approximate sense of direction until you find something you like" that defines how LLMs work.

The definition of slop is poor taste. By that definition a lot of human work is also slop.

But that also means that in spite of the technical crudity, it's possible to produce interesting AI work if you have taste and a cultivated aesthetic, and aren't just telling the machine "make me something interesting based on this description."

Robocalls nowadays tend to wait for you to break dead air before they start playing the recording (I don't know why.) So I've recently started not speaking immediately when someone calls me, and if after 10 seconds the counterparty hasn't said something I hang up.