I think that mostly depends on how good a writer you are. A lot of people aren't, and the AI legitimately writes better. As in, the prose is easier to understand, free of obvious errors or ambiguities.

But then, the writing is also never great. I've tried a couple of times to get it to write in the style of a famous author, sometimes pasting in some example text to model the output on, but it never sounds right.

I think it is also fairly similar to the kind of discourse a manager in pretty much any domain will produce.

He lacks (or lost thru disuse) technical expertise on the subject, so he uses more and more fuzzy words, leaky analogies, buzzwords.

This maybe why AI generated content has so much success among leaders and politicians.

I think it’s essential to realize that AI is a tool for mainstream tasks like composing a standard email and not for the edges.

The edges are where interesting stuff happens. The boring part can be made more efficient. I don’t need to type boring emails, people who can’t articulate well will be elevated.

It’s the efficient popularization of the boring stuff. Not much else.

Mediocrity as a Service

> But it's the jagged edges, the unorthodox and surprising prickly bits, that tear open a hole in the inattention of your reader, that actually gets your ideas into their heads.

This brings to mind what I think is a great description of the process LLMs exert on prose: sanding.

It's an algorithmic trend towards the median, thus they are sanding down your words until they're a smooth average of their approximate neighbors.

I'm sure this can be corrected by AI companies.

Regurgitative AI

People want the real thing, not artificially flavored tokens.

I would rather read the prompt than the generative output, even if it’s just disjointed words and sentence fragments.

> most of the time you only get what the most boring person in the most boring cocktail party would say

don't be mean, it's median AI à la mode

> The "AI voice" is everywhere now.

Maybe I'm going crazy but I can smell it in the OP as well.

Yes, I get more and more visceral reactions to it. I'm reminded of JPEG artifacts - unnoticeable in 1993!

same. it is showing how many people are not trying to participate - just appear to. I want to read from and write for my peers, but it seems we are just awash with fakers

> Yeah, AI could not come up with that phrase.

Agreed.

"AI" would never say "loins" (too sexual)

"AI" would never say "dogmatism" (encroaches on the "AI" provider's own marketing scheme)

A sloppy mixed metaphor?

I've found LLMs to be terrible with ideation. I've been using GPT 5.x to come up with ideas and plot lines for a Dungeon World campaign I've been running.

I'm no fantasy author, and my prose leaves much to be desired. The stuff the LLM comes up with is so mind numbingly bland. I've given up on having it write descriptions of any characters or locations. I just use it for very general ideas and plot lines, and then come up with the rest of the details on the fly myself. The plot lines and ideas it comes up with are very generic and bland. I mainly do it just to save time, but I throw away 50% of the "ideas" because they make no sense or are really lame.

What i have found LLMs to be helpful with is writing up fun post-session recaps I share with the adventurers.

I recap in my own words what happened during the session, then have the LLM structure it into a "fun to read" narrative style. ChatGPT seems to prefer a Sanderson jokey tone, but I could probably tailor this.

Then I go through it, and tweak some of the boring / bland bits. The end result is really fun to read, and took 1/20th the time it would have taken me to write it all out myself. The LLM would have never been able to come up with the unique and fun story lines, but it is good at making an existing story have some narrative flare in a short amount of time.

> I think AI writing is better used for ideation

It shocks me when proponents of AI writing for ideation aren't concerned with *Metaphoric Cleansing* and *Lexical Flattening* (to use two of the terms defined in the article)

Doesn't it concern you that the explanation of a concept by the AI may represent only a highly distorted caricature of the way that concept is actually understood by those who use it fluently?

Don't get me wrong, I think that LLMs are very useful as a sort of search engine for yet-unknown terms. But once you know *how* to talk about a concept (meaning you understand enough jargon to do traditional research), I find that I'm far better off tracking down books and human authored resources than I am trying to get the LLM to regurgitate its training data.

> Semantic ablation is also why I'm doubtful of everyone proclaiming that Opus 4 would be AGI if we just gave it the right agent harness and let all the agents run free on the web. In reality they would distill it to a meaningless homogeneous stew.

I'm so glad that you have given me the language to express this perspective.

> "NEVER use generic AI-generated aesthetics like overused font families (Inter, Roboto, Arial, system fonts), cliched color schemes (particularly purple gradients on white backgrounds), ...

Now, imagine what happens when this prompt becomes popular?

Keep in mind that LLMs are trying to predict the most likely token. If your prompt prohibits the most likely token, they output the next most likely token. So, attempts to force creativity by prohibiting cliches just create another cliche.

Several days ago, someone researched Moltbook and pointed out how similar all the posts are. Something like 10% of them say "my human", etc.

In recent months(?) I've more often noticed HN story titles changing over time.

I'm not sure what's driving this. It reminds me of SEO.

People felt this was already happening. I remenber reading filter world which described this pre Ai

The middle gets lower and lower with every passing day.

I call it the great blur.

The argument is that the best writing is the unexpected, while an LLM's function is to deliver the expected next token.

I remember an article a few weeks back[1] which mentioned the current focus is improving the technical abilities of LLMs. I can imagine many (if not most) of their current subscribers are paying for the technical ability as opposed to creative writing.

This also reminded me that on OpenRouter, you can sort models by category. The ones tagged "Roleplay" and "Marketing" are probably going to have better writing compared to models like Opus 4 or ChatGPT 5.2.

[1]: https://www.techradar.com/ai-platforms-assistants/sam-altman...

I mean there's tons of better-writing tools that use AI like Grammarly etc. For actual general-purpose LLMs, I don't think there's much incentive in making it write "better" in the artistic sense of the world... if the idea is to make the model good at tasks in general and communicate via language, that language should sound generic and boring. If it's too artistic or poetic or novel-like, the communication would appear a bit unhinged.

"Update the dependencies in this repo"

"Of course, I will. It will be an honor, and may I say, a beautiful privilege for me to do so. Oh how I wonder if..." vrs "Okay, I'll be updating dependencies..."

That's like asking why McDonald's doesn't improve the quality of their hamburger. They can, but only within the bounds of mass produced cheap crap that maximizes profit. Otherwise they'd be a fundamentally different kind of company.

It's a hard problem and so far not a profitable one (I hope the solution will emerge as a byproduct of another innovation)

https://nostalgebraist.tumblr.com/post/778041178124926976/hy...

https://nostalgebraist.tumblr.com/post/792464928029163520/th...

I kind of think of that as just increasing the standard deviation. Its been a while since I experimented with this, but I remember trying a temp of 1 and the output was gibberish, like base64 gibberish. So something like 0.5 doesn't necessarily seem to solve this problem, it just flattens the distribution and makes the output less coherent, with rarer tokens, but still the same underlying distribution.

you have to know that your "simply" is carrying too much weight. here's some examples of why just temperature is not enough, you need to run active world models https://www.latent.space/p/adversarial-reasoning

When applied to insightful writing, that is much more likely to dull the point rather than preserve or sharpen it.

Look through my comment history at all the posts where I complain the author might have had something interesting to say but it's been erased by the LLM and you can no longer tell what the author cared about because the entire post is a an oversold monotone advertising voice.

https://news.ycombinator.com/item?id=46583410#46584336

https://news.ycombinator.com/item?id=46605716#46609480

https://news.ycombinator.com/item?id=46617456#46619136

https://news.ycombinator.com/item?id=46658345#46662218

https://news.ycombinator.com/item?id=46630869#46663276

https://news.ycombinator.com/item?id=46656759#46663322

https://news.ycombinator.com/item?id=46661936#46663362

https://news.ycombinator.com/item?id=46748077#46749699

Have you not seen it any time you put any substantial bit of your own writing through an LLM, for advice?

I disagree pretty strongly with most of what an LLM suggests by way of rewriting. They're absolutely appalling writers. If you're looking for something beyond corporate safespeak or stylistic pastiche, they drain the blood out of everything.

The skin of their prose lacks the luminous translucency, the subsurface scattering, that separates the dead from the living.

Kaffee: Corporal, would you turn to the page in this book that says where the mess hall is, please?

Cpl. Barnes: Well, Lt. Kaffee, that's not in the book, sir.

Kaffee: You mean to say in all your time at Gitmo, you've never had a meal?

Cpl. Barnes: No, sir. Three squares a day, sir.

Kaffee: I don't understand. How did you know where the mess hall was if it's not in this book?

Cpl. Barnes: Well, I guess I just followed the crowd at chow time, sir.

Kaffee: No more questions.

It is an opinion piece. By a dude working as a "Professor of Pharmaceutical Technology and Biomaterials at the University of Ferrara".

It has all the tropes of not understanding the underlying mechanisms, but repeating the common tropes. Quite ironic, considering what the author's intended "message" is. Jpeg -> jpeg -> jpeg bad. So llm -> llm -> llm must be bad, right?

It reminds me of the media reception of that paper on model collapse. "Training on llm generated data leads to collapse". That was in 23 or 24? Yet we're not seeing any collapse, despite models being trained mainly on synthetic data for the past 2 years. That's not how any of it works. Yet everyone has an opinion on how bad it works. Jesus.

It's insane how these kinds of opinion pieces get so upvoted here, while worth-while research, cool positive examples and so on linger in new with one or two upvotes. This has ceased to be a technical subject, and has moved to muh identity.

People haven't really spoken about the obvious token manipulation that will be on the horizon once any model producer has some semblance of lock-in.

If you thought Google's degredation of search quality was strategic manipulation, wait till you see what they do with tokens.

Going off search results, it seems to be a new coinage. I found mostly references to TFA, along with an (ironically obviously AI-written) guide with suggestions for getting LLMs to avoid the issue (just generic "traditional" advice for tuning their output, really). The guide was apparently published today, and I imagine that it's a deliberate response to TFA. But FWIW the term "semantic ablation" does seem to me like something that newer models could invent

At any rate, it seems to me like a reasonable label for what's described:

> Semantic ablation is the algorithmic erosion of high-entropy information. Technically, it is not a "bug" but a structural byproduct of greedy decoding and RLHF (reinforcement learning from human feedback).

> ...

> When an author uses AI for "polishing" a draft, they are not seeing improvement; they are witnessing semantic ablation.

The metaphor is very apt. Literal polishing is removal of outer layers. Compared to the near-synonym "erosion", "ablation" connotes a deliberate act (ordinarily I would say "conscious", but we are talking about LLMs here). Often, that which is removed is the nuance of near-synonyms (there is no pause to consider whether the author intended that nuance). I don't know if the "character" imparted by broader grammatical or structural choices can be called "semantic", but that also seems like a big part of what goes missing in the "LLM house style".

Bluntly: getting AI to "improve" writing, as a fully generic instruction, is naturally going to pull that writing towards how the AI writes by default. Because of course the AI's model of "writing quality" considers that style to be "the best"; that's why it uses it. (Even "consider" feels like anthropomorphizing too much; I feel like I'm hitting the limits of English expressiveness here.)

These kinds of prompts don’t really improve the writing IME. It still gets riddled with the same tropes and phrases, or it veers off into textual vomit.

Do the terms *Metaphoric Cleansing*, *Lexical Flattening*, and *Structural Collapse* that the author provides have equivalents in LessWrong's parlance?

I think you might be missing the point of the article.

I agree that the term "semantic ablation" is difficult to interpret

But the article describes three mechanisms by which LLMs consistently erase and distort information (Metaphoric Cleansing, Lexical Flattening, and Structural Collapse)

The article does not describe best practices; it's a critique of LLM technology and an analysis of the issues that result from using this technology to generate text to be read by other people.

Have you considered that your analysis skills may not be keen enough to detect generic or boring prose?

Is it possible that what is a good result to you is a pity to someone with more developed taste?

Why don't you post it so we can see how much better the AI made it?