> But if I want to communicate with humans, my humanity has to shine (or at least ooze) out of my prose.

Yeah. I want to be able to touch a human being (or at least have one touch me) through the words. LLMs poison that, even if a human prompted the LLM.

Translation... I think I object to translation less than Martin does. There's a human back there, and they're writing all the words, and then an LLM is translating them. The LLM may not translate them perfectly, but they aren't going to introduce "load-bearing" when it wasn't there.

The bit about using LLM when you're writing for a bureaucratic process rather than a human was interesting, at least to me. I hadn't thought of it that way before, but I think it makes sense.

Thank you for posting this. I don't have a lot to add on the subject, but I found it interesting and thought-provoking given the age we live in. I find it really hard to tell real human and LLM text apart, even though some people claim they can, calling it "AI slop".

Yawn. This subject has been discussed ad nauseum. The lines are drawn. I'm over it.

So many "load-bearing seams"...

People have been talking about the importance of curation for more than a decade.

Because many good ideas are not immediately obvious good ideas, in an environment at risk of information overload, we need some way to quickly vet ideas to see whether they are worth investing the time to consider them. (It is often the case that the best ideas require considerable investment of time and effort to be judged as good, and, it's also often the case that this determination can be made only at the very end of such a process.)

“Don't judge a book by its cover,” sure, but, historically, there have been superficial markers of care and attention that we've used to these ends. These are obviously very flawed signals, but, you know, maybe if you took a lot of time to craft a message, I don't feel that bad if I waste a bunch of time trying to decode it. These markers—attention to detail (e.g., spell and grammar checking,) clear-structure (e.g., “bottom line up front,”) and typographical flourishes (e.g., smart quotes and em-dashes and extended unicode like diæreses…)—were an indication of some modicum of quality control and care. I, myself, used to brag about or show-off having set up .Xcompose keybindings (https://wiki.archlinux.org/title/Xorg/Keyboard_configuration...) which was just rare enough across the general population to be noteworthy.

All of this that was once somewhat expensive is now cheap or basically free.

Even if I make a point of going back to a pre-UTF-8 Usenet style (or even intentionally insert run-on sentences or misspellings or grammatical errors,) the cost of an LLM to style-transfer its output to match this is barely much of anything. All it means is I wrote one prompt for the first draft, and then one more prompt for the refinement.

Additionally, some LLM habits are actually good habits. Putting the bottom-line up front (in bold, even!) is a great habit in a business e-mail! Decision-makers genuinely struggle with reading through long paragraphs of detail!

Unfortunately, the problem with the LLM output is that it takes good, valuable, superficial or structural habits that it can cheaply apply, and it drapes those around inane, banal, cloying, muddy, meandering meaninglessness.

Today, we can maybe still judge a book by its cover, because even one more prompt worth of refinement is still too much effort, but I don't think that's amounts to much, since the frequency and loudness of complaints of LLM writing means that model trainers are going to train their models to adapt to whatever signals we use for quick vetting. I am skeptical whether this training will concurrently improve the novelty, innovativeness, or other intangible qualities of the actual message. (With the models, as they exist today, and as we use them today, at least in the field of software development, I see extremely poor capability at generating innovation… Personally, I don't even like the quality of the Python code they spit out, but they sure do type real fast, and, like, pejoratives aside, unexciting, mediocre, corporate-y code does have a real place in the world.)

So what happens when the economics of production in the medium significantly outpace the capabilities of production in the message? The LLM can write a comment nearly indistinguishable from any given human… except when you actually take the time to read through it, it's just inane drivel. Thoughtless and a waste-of-time. And there's basically no way to manage your investment of time and effort to tell one way or the other.

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An important distinction as AI-generated content becomes more common: having a consistent voice is not the same as having a distinctive point of view. The best AI-assisted writing should preserve the author's personality, judgment, and expertise rather than simply imitate their vocabulary or sentence structure. This is a useful concept for anyone building content workflows with LLMs.

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