I thought it was an interesting comment and I suffer from the same issue when trying to get a clear idea from my head into writing. I sacrificed some Pangram credits to reply to the parent comment, fwiw.

That's interesting. How does the tooth fairy feel about it? The Easter Bunny?

Sometimes I can find it if I keep revising, but staring at a sentence too long can entangle you in a particular linguistic frame that can be hard to escape.

This expresses quite well an experience that I have sometimes had. Your first words can trap you in a box that you know isn't right, but you can't figure out how to escape.

I don't want to sound offending, but if that is your own writing style, it makes sense why AI writing resonates with you.

AI is very bad at properly handling statements that make heavy use of vague quantifiers (e.g. "some", "most") and also commits a lot of pretty serious logical fallacies. It is also bad at handling subtle logical negation, generally.

One of the most egregious negation issues I run into a lot is when I (or someone) makes a statement of the form: "not X" or "X is thus not true", and the AI then proceeds to interpret or summarize this as 'whatever is the opposite of X is the case'". This will cause it to go down a useless path investigating or disputing the opposite of X, which generally has no relevance or bearing on anything.

It also often very harmfully will replace your carefully chosen words with weirdly specific academic operationalizations or formalisms, then again waste huge amounts of text refuting / showing "problems" that result from that formalism, all of which again have no bearing or relevance on the original statement. An example would be you saying something like "intelligence, generally, must surely explain some of the differences in X", and then it will go "actually IQ does not correlate with X", unless you specifically tell it not to conflate psychometric IQ with intelligence generally.

Sometimes this is helpful, but the more specific / technical the domain, the more often you specifically have to prevent it from going down stupid paths that should be obvious given the expert context and wording, because it can seem almost hungry to try to catch you in some kind of insipid 'gotcha'. Much of these issues often clearly arise immediately from the first-pass "reword what the user said" part, given the reasoning traces.

A big part of the reason I'm deeply resentful of AI is that I want to be an artist not an editor

And also to think in different directions than you would have gone in isolation. Regardless, this is a great heuristic / rule that I will be sharing and keeping in mind.

This is brilliant, I'll be using that. Thanks.

I don't find AI helpful for truly constructive or creative prose, it devolves to the median output very quickly and it's almost painful to hear in my inner voice when I read out an AI-ified idea. The "not this but that" formulation is especially bad or cloying attempt at pretending to refine an idea or lightly correct something to start from a position of authority, very annoying.

That said, I have a lot of situations at work where I am asked to simplify something I am an expert in or convey something for a different audience, particularly as I prepare for presentations and I do find it helpful in helping me step down my writing or work. YMMV.

I also skip words a lot and can miss that even after 2-3 editorial passes and it's very helpful at that vs. say normal spellcheck.

It was the topic I was discussing with the person I replied to.

If you want to discuss something else just let me know.

Writing is thinking. In many situations where we are called on to write, what is actually needed is thought.

Excellent idea, I'll try this. And for those who don't speak a second language other than English, you could use leet.

Wow, love this! Could even match with a language I am actively learning so it helps that as well!

I use an antagonistic agent to review and refute its findings, works great.

It's funny. I always thought that writing was meant to inform, persuade, or entertain about the subject at hand.

But in professional settings, a lot more of the informativeness is about the author, and a lot more of the persuasiveness is I'm worth your time and money. So, if the author is an LLM, and obviously so, what exactly are you informing your audience of (about yourself), and what are you persuading them to do (with your article).

I think we now know.

Ant tips for doing so? I've never been able to do that, even as a kid. I remember the meaning but not the exact words.

Humans sometimes don't remember things 100%. LLMs can produce certain text and be run deterministically.

I don't really understand this side of the debate other than as a gotcha tbh.

If LLM use atrophies your brain and skills that's bad. If it has a repulsive writing style that's bad.

I'm not sure what the debate about whether an AI is a statistical parrot unlike humans accomplishes. Is relying 100% on a bad human speechwriter somehow better?

It doesn't hold knowledge it has the ability to seive through a lossy latent representation of knowledge.

Except that there are such things as speech writers, whose job is literally to write prose in the voice of the person who’s going to read it.

And there are countless other jobs out there where people are the “voice” of another party. From those who manage social media presence, to PR firms, to copyrighters, all who put statements out on others behalf.

There was already an industry of professionals whose job it was to have others write things on our behalf, and that existed long before LLMs were a thing. What LLMs did was make that that service available much more cheaply.

Same flow as translating from your native tongue to a foreign language.

Going from foreign to native is easier than native to foreign. The latter requires completely different brain paths and a lot more understanding of the language to actually get to something correct.

That’s not entirely true. High end models will point out things that are misrepresented or plain wrong, and this will often be accurate.

Here I would maybe argue that the simplicity makes these kinds of tasks tedious (like doing taxes), so it also makes a lot of sense for people to want to just throw AI at it. Tedious, easy rote tasks can be much more unpleasant than engaging ones.

I think people massively over-rely on AI and I really worry about the consequences of this. But there is nothing baffling at all about the basic appeal, IMO.

It depends on what you are trying to convey and how.

Hmm, when do you suppose HN started (ab)using the downvotes reddit-style anyway? I don't recall seeing hardly any downvotes before 2023.

this is where I perhaps differ from other commenters, I do not have higher tolerance, regardless of whether I asked it or am reading something someone else posted, fully or partially written by LLM.

Perhaps English is not your first language? To people who are well-read, LLM-ism grate on their nerves like the high-pressure sales pitch you hear on 3am shopping channels.

Moreover, when you wrote it yourself, you are intellectually responsible, you can defend it, while if the LLM wrote it for you, nobody is really responsible: The LLM didn't write it "out of its own motivation", it was merely extrapolating your prompt; and you only provided the prompt, not the extrapolation.

So the intellectual responsibility is diluted to a substantial degree: The text is not the opinion of anyone who can be expected to honestly defend it. It's bullshit in the technical sense.

so you never use LLMs like at all?

When I seek input (whether passively or actively; it doesn't matter) from another person, then I generally want that person's input. I want their words.

There's a (probably subconscious) reason for you wanting that (and I just posted this elsewhere on a different story, but it bears repeating): https://news.ycombinator.com/item?id=49767867

That's logical, yes. If person A wants to read a summary (even a generated one) outlining the state of modern space dinosaur research, or whatever, and person B also wants the exact same thing, then person B should be happy to read the summary that person A prompted.

The same is true if the thing they both want is a stale cupcake bought from a gas station. But this still doesn't mean that person B wants to be presented with person A's stale thing, because the overhead of the social transaction isn't worth the object. Which is to say, it makes a terrible gift.

But the reader cannot be sure they ARE understanding what you are trying to convey. Maybe the LLM added something you never thought of, maybe it made form to your various thoughts in a way that isn't actually true to the way you would have communicated them if you did it yourself.

The exercise of writing is an important step of actually understanding your own thoughts properly. Readers are reading to get a sense of your own experience and ideas on the topic, if it turns out you didn't actually write the final thing yourself they'll always feel a sense – in whatever way – of being conned by the author. Intentional or not.

Technical documentation, where what the reader wants to come away with is the clear facts on what something does and how to use it/whatever, is a different matter. But even then editing is key – overlong text, unnecessary information, these will again fight against what the reader is there to get.

With that said, if you do want to do it, a disclaimer is at least upfront and may well show there's no intentional attempt to deceive.

TLDR: Communication from a human is not only the material being communicated, it's also a signal that the human understands the message they are sending you. When you pass that message via an intermediary (regardless of whether the intermediary is a human or an LLM), the receiver is never sure that what they received is what you intended.

100% right. The fundamental point here is:

- it's OK to be wrong

- it's OK to be wrong and not know

- it's a little less OK to be wrong when you should have known

- it's much less OK to be wrong when you haven't put any effort into being right

- it's far less OK to be wrong when you are pretending to know either way

Relying on AI writing risks pushing you further down the path to worse kinds of wrongness.

I think that's somewhat reasonable, but not always: the effort of researching and of writing yourself will teach you better then reading something someone else wrote, especially an LLM that will get things wrong in surprising ways that a human would not.

I'm not saying you should never do this: our time is valuable, and we shouldn't spend it on things that are not genuinely worthwhile to us if we can help it. But we're still losing something by having an LLM write for us, even if the intended audience is just ourselves.

This is 100% my take. It's nice and easy.

I do not personally even think AI is useful for editorial purposes; I would rather read and re-read my own text and edit it down (which, despite my overlong comments here, I actually do), than have someone else do it. The reason is simple: editing is my search for the clearest way to express my thoughts.

I tend to turn grammar checkers off, but leave spelling checkers on, because they catch typos and a handful of my spelling gremlins like "liaise".

I think about this a lot. I started my teenage-to-young-adult life in the literary world as a poet who loved programming, and made a living as a ghostwriter. At some point, I fell in love with mathematics and wound up in ML in research/engineering for the last 8 years or so. So, I've thought a lot about writing and ML and their intersection.

I think one of the underappreciated things about writing "substantive" work is that the work you see at the end isn't the first attempt. And I don't mean the first draft of the piece. I mean that almost always, writers iterate on the same topic many times, either with complete published pieces or abandoned drafts or even just conversations and sessions of unproductive daydreaming. It's a cliche that your best work typically also comes out fastest, but it's not because of divine inspiration, it's because you've whittled the big idea you actually care about down so much in your mind that you instinctively know exactly how to write it.

My experience has been that for people who don't work this way or don't write a lot, LLMs can give them this incredible feeling of leaping straight from inkling to "substantive" writing. And because they haven't built up those muscles or "taste", they don't immediately recognize that it's imprecise and hard to follow.

That's not to say they're not brilliant in their own right, just that they haven't spent a lot of time on this particular thing. Sort of like a very gifted programmer who doesn't have a ton of experience yet (speaking as someone who is gifted at nothing and frequently has to do things they're inexperienced at).

So I don't think the problem is that LLMs just write bad. It's that LLMs are so wonderfully powerful that they allow you to confidently leap forward to create something that is a little beyond your experience.

And that's why I have different reactions to heavily AI generated writing. When it feels like marketing at scale, it grosses me out. But when I feel like it's just someone who is excited to write an idea and maybe doesn't have a lot of experience doing it, I'm not judgemental. My hope is that it makes them more excited about writing, and that trying to make their next piece better will lead them inevitably to start thinking about where the last piece fell short. And if there's some slop along the way, eh, I'm not compelled to read it.

PROMPT: make this more concise while conveying the same message

I think “cutting down on words” depends on personal writing style. AI tends to write more than I would, so I usually have the opposite problem.

Yes, you hit on the main problem with LLMs for writing. It doesn't cost you anything. It can do the reasoning for you and creating long messaging with dodgy reasoning.

I think you should write down your thoughts, tell it to make the points you spelled out and be concise and respectful of the reader's time and attention. When I do this, it works very well. But I agree if you just have it respond and reason it tends to often produces bad responses.

Communication between two people is hard enough. Involving another actor as an intermediary rarely helps. It quickly becomes a game of telephone. Doubly so if the intermediary is an LLM.

Programs must be written for people to read, and only incidentally for machines to execute. -- Hal Abelson

My reason: code can be checked objectively. I can run it and confirm it works.

Running the code only confirms that it works with the precise input, in the precise environment, under the precise circumstances you run it under. It doesn’t ensure that the code is correct. Thinking through the code, on the other hand, lets you consider all possible cases. It’s the difference between experiment and (mathematical) proof.

For an objective correctness proof, using a formal language is indispensable.

Well, there's some nuance. I'm not really proud of my code. I'm proud of the software that gets built with it.

Code is just sitting in a git repo somewhere, not necessarily running. That's a big distinction for me. Consider that code volume has increased 14x on github this year, but we see nowhere near that increase in the actual usable software. Code is cheap and getting cheaper. Running software ain't.

Another way to put it: I'm only interested in code so far as the value it provides. That value, not the code itself, is the source of pride. If I can provide similar value without any code at all, I'd gladly do so.

I understand the distinction you're drawing, but in my experience, it gets a lot blurrier as a project evolves. Caveat that I use LLMs all day and have for quite some time, so I'm coming from a positive perspective.

The danger for me in LLM code is the same as in writing, it's just that I'm not typically writing at the same scale as when I'm building something. The final piece when I'm writing is usually a message or a 1,000 word article at most. So I'm naturally going to analyze it quite intensely, because I can afford to. And I don't really use LLMs for this at all. I use them for things around writing (research, interrogating ideas, situationally specific stuff, mapping, visuals, publishing, etc.) And the code equivalent to an essay or message would probably be something like a single script, or the sort of thing I'd write as example code when I'm teaching. In those settings, again, LLMs can be helpful, but I'm still going to be really opinionated at a highly detailed resolution.

But a codebase is more comparable to a novel than an essay. Or more directly, the writing in a codebase is usually the documentation, which grows commensurately with the codebase. And the real LLM risk here is the drift that can happen over the course of many epics or "chapters" as the LLM writes "code that works but is imprecise and probably shouldn't work this way" or introduces weird new terminology that neither of us can precisely define. Worse, this usually becomes obvious down the line, and I have to parse through the verbose constructed world the agent has created to trace the issue back. That's a big cognitive tax, because I'm holding these weird parallel worlds of "How did the LLM's alien brain get here within the bounds of the contracts" and "What do I really want this to look like".

So I think it's fundamentally the same phenomenon, and we're all developing our skills around working with it in real time.

I was about to reply with the exact same sentiment. How often may be different from writing but writing code is often thinking.

I can't help but think that some of the arguments here apply equally well to writing _code_ as they to to other substantive writings.

All of the arguments here apply to writing code; Why would the quality of writing differe when writing prose vs code?

I cannot read LLM slop prose without getting mentally fatigued trying to figure out what it is saying, and I've discovered that the quality of the code it produces has the same effect on me.

I don't know what everyone else is doing but I've certainly been writing a lot more often because of my interactions with AI. Maybe some people used to write more but have been leaning on AI so they can write less but I don't think that's universally the case.

I agree that being lazy about writing and simply using a short prompt or list is detrimental if you are replacing your own output but you don't have to do that and you don't have to accept any of the output either. The best chats I've had usually start with a large amount of my own writing up front, thinking through the idea, listing several alternative ideas, asking a lot of questions, jotting down related topics, etc. and then reading the resulting output and critiquing it, asking for clarification, doing my own research on it, even just discussing it with the AI and doing this for a number of turns until I feel like I've exhausted the topic in the chat. I then often take what I've shaped in my own mind from that process and write something myself, either that or take the best parts of the output and edit, rearrange, reinterpret, and add to it in order to produce something.

I guess it all comes down to whether you are actively engaging with the material, regardless if that material is the result of a Google search, pulled from a book or generated by AI. If you just read it or copy paste it and don't engage with it and think about it then it doesn't do much good.

I suspect that between the advice in this article and your own...people fear that thinking is the only thing they have left to do in order to write.

Who cares? AI gives me lots of text and it's not like it's ever going to go away. Any time in my life in the future when I need text, I can just have AI generate it. Thinking is solved.

People were self-conscious about their writing (quality, speed) before LLMs. AI seems like a way out of that weakness. That's just the nature of a tool: it seems to extend our ability. People are going to use it if they don't have a better option.

The only cure: be the person people can turn to instead of the machine, whether that's helping them write or helping them become better writers. And, yeah, you'll have to do that cheaper than the AI (netted against the social cost of being the guy/gal who got caught slopping).

This absolutely doesn't look like AI writing and this is a pretty weird signal to use.

I have a much better theory: as it happens, Substack automatically converts "--" to em-dash as you type. I'm guessing the author knew that and wanted to use this feature, but composed the article in another editor and then copied-and-pasted the whole thing. Because of how Substack implements the feature, the substitution doesn't actually kick in on Ctrl-V, so all these "--" remained in place.

Haha, I do actually use double dash, as I write in org-mode. I then convert that org-mode text to Markdown and copy and paste the Markdown into the Substack editor, which preserves the double dash. In case it needs to be said, the article is 100% human-written.

i like to write and then workshop with the ai. i find that i have to push back on tendencies it (has less of these days) has to rewrite my work in massive ways. it also can help with writers block sometimes, or parts of a paragraph i'm not that interested in.

Every time there are some articles like this, some people replied with: "Yeah I agree, but I use LLMs 'this way' and my way doesn't suffer from the problem mentioned." I'm not saying these people are wrong but it's interesting that people always think their way of using LLMs is the best (and other people are using LLMs in the wrong way).

I know I'm an outlier but i truly just can't imagine using an LLM for absolutely anything outside programming itself. I'm something of an expert in programming so i can spot the hallucinations and nonsense. I'm not an expert in lots of other things where i wouldn't be able to spot the problems. That and the writing is so grating and awful, the docs are so verbose yet vague. Reading the responses i explicitly asked it to generate is bad enough, i could never ask someone else to do it with something they could just generate themselves.

This has to be the most toxic topic ever

I disagree, but think that there's a specific way to use AI to write. I use AI to get critiques on my writing, but never want it to actually produce text for me. I experimented with this by making https://www.writelucid.cc, and I really like it, I use it for all my writing nowadays.

Getting comments on specific sentences/passages and being able to debate them has been great for my thinking, and I've learned a lot about what works and what doesn't work while writing with it.

It blows my mind that anyone would ever use an LLM to write, if communication is their goal.

LLM writing takes your prompt and adds stuff you didn't write, burying your meaning and making it harder for the reader to get your message.

Way more effective to just publish the prompt.

Of course, it's great if you're just trying to fill space or satisfy demands of a bullshit job.

When I was a graduate 20 years ago, the partner would sit with a print out of every word I wrote and a red pen marking every change required, as the partner knew anything that was written in his name had to be exactly what he would have said.

Same needs to go for LLMs, leverage them for productivity both make sure you are owning their output.

This resonated with me - I’ve been avoiding things that affect my word choice since mobile messaging apps started introducing “next logical word choice” features for the exact reasons outlined here. If writing approximates thought, I don’t want some third party process constantly butting in and derailing that train, even if I’m just texting someone.

But as a thought experiment, replace “writing” in this article with “coding”, and ask yourself why the same points wouldn’t hold? Maybe it’s because I enjoyed writing essays in college, before I wrote software as a job, but I’ve never seen much difference between writing code and writing natural language. They’re both a means to an end, some lossy way of encoding thought. One is machine executable and one isn’t (arguable maybe, at this point) but I think they both suffer from the same pathologies outlined here when AI takes over, eg, “writing is thought”, “AI writing can be subtly wrong” etc.

Yet the preponderance of hot takes I see these days are along the lines of “code review is dead”, “writing code by hand is dead”. Perhaps people largely don’t see code as communication? It bothers me to imagine a world where the vast majority of textual communication produced becomes machine generated, whether that’s natural language or code.