I strongly disagree, for reasons that are different from what's discussed in the thread.

It doesn't matter if LLMs can't hold a candle to some of the finest human writing. What matters is that before you become a world-renowned writer, you need to pay your bills, often for a long time. And that's what LLMs take away. All the mundane literature-adjacent works: journalism, translations, technical writing / corporate comms, copyediting.

The same goes for many other forms of art. A budding painter or a musician could support themselves off commissioned / commercial work while working on their grand opus... but now, the customers just prompt gen AI.

There may well be some writing jobs that are safe for the reasons given in this post, but that doesn't help all the writers I know who have already lost their jobs and are struggling to find work.

It's not enough that humans can tell the difference and feel an ick, there also need to be enough organizations willing to pay money for that difference. From my vantage point, there are not. It turns out that for a ton of the writing produced by companies, the quality of the prose wasn't really "load-bearing" as Claude puts it. That writing is there to occupy a space and look professional at a glance, the same way elevator music is tolerable for the duration of an elevator ride.

I have a feeling the author framed this the wrong way.

my take is - the author wanted to express that there's always a demand for human prose / writing that captures the subtleties of expression, thought & ideas. which is very true. & we can already see this e.g by people opting out of LinkedIn for it's A.I driven long posts. whereas engagement is high on X where posts are likely to be human generated.

I wouldn't say writing as a job is protected - as corporations will always take shortcuts.

No. Below is my emotional opinion based on my own experience:

Currently the models are totally helpless with plot and emotions.

They make epistemic, logistical and temporal mistakes.

But:

They are very good in finding _your_ epistemic, logistical and temporal mistakes. They are great as adversarial reviewers. They can validate grounding. They can test voices. They can build complex parallel reconstructions representing timestamped inner monologues, dialogues and events.

A process consisting of many models representing characters + states + epistemic isolation + reviewers could be great to validate or disprove ideas.

Models can write you formal models of your writing. They can run solvers against your formal models to - again - validate and disprove ideas. Literally, you could validate your writing with TLA+, SAT solvers, queries against formal ontologies.

They can build corkboards of unimaginable complexity.

A moderately good writer who plays well as a part of human-machine writing apparatus could be a competitor even for a top-tier author.

When you write with a model, you define disciplines. You write your prose. You let your model turn your prose into formal systems and verify it. You let your model criticize you. Sometimes you let your model to polish your crude language into something sleek.

So, at current state of LLM development, writer as source of ideas and governing process is safe. Writer as a vision-expansion machine is not.

I've been very hostile to LLMs in literature. I've started to write a short novel about that. As a joke I tried to use models after half of the text was done. I've applied my engineering skills. That changed both me and my opinion. Models are awesome if you know their advantages and weaknesses.

A so-so writer with a good model and a good approach to prose development could produce epic stuff.

A lot of the replies are insisting AI will get better at writing with more development but I don't see it. Even if you have a mathematically perfect writing AI you still run into the same problems you would have if you handed off your writing task to someone on fiverr or something. It can't magically know what you want to say, it only has the information you gave it. A prompt complex enough where it won't get any wrong ideas has to contain as much information as the output would have.. so just write it.

Workaday copywriting is dead (was moribund, now has been shot in the head). Prestige literary writing is zero-sum and therefore eternal, in the same way that equities trading (not formation and not business-building) is zero-sum and therefore the actual success of LLM has not given anyone a signal advantage anywhere there, because everyone else has LLM too.

Has writing ever been a safe job, even before AI?

The real thing being said here is that the author can tell good writing from bad, and assumes everyone can... I'm a visual artist and at generative art is blindingly obvious.... To me.... But not to many folks around me! Including a few artists and art adjacent folks.

If I found the best human writer in the world, and had him write every article on the internet, wiping his memory between each one, his writing would start to get pretty noticeable and boring!

The safest jobs from AI are the Unionized ones.

The safest jobs are the ones that don't feed a training algorithm with data, any work that remains more of a mystery.

Mmm, I doubt it.

I have a weird background (product, development, writing + devrel). I write a lot of code and a lot of articles.

I think there is a lot of overlap in how people who write code or articles (documentation, books, etc.) use AI. On one side of the spectrum, you have people who just blindly input some prompt, accept the output and move on with their lives. You can likely predict how that is going for them (not great). On the other side of the spectrum, you have people who outright reject all AI and are continuing to plod on with how they have always done things.

In the center is a more reasonable approach that leverages AI to create without blindly accepting the output. This applies very much to writing.

The workflow that I've adopted over the past two years or so has been to leverage AI to help with the research and outline process. Once I'm happy with the structure I go and I write what I need to write.

This maps pretty closely to the code that I write. It's fine.

Safest job from AI is also the oldest job.

I don't think writing is an "AI-complete" problem. It's just that the models are inefficient at it right now, due to training and/or architecture. Good writing requires thinking, reflection, and doing multiple passes. This currently translates to using a high reasoning effort and burning lots of tokens, but big AI labs like Anthropic are struggling with handling the load, so they're doing the exact opposite and finding whatever cheap trick they can to reduce token usage. One such trick is condensing ideas into as few tokens as possible, which in my opinion is one of the reasons Claude sucks so much at writing.

Yeah I had GPT5.6 Sol act as an editor on my new chapter and it did find decent issues with PoV and narrative distance but it was so bad at prose. The best way I can describe it is, it was too clean. Often times humans write about things unsaid and left for reader to interpret or deliberate awkward sentences to tease character psychology. The AI just straight up corrects these without second thought. This will likely be the case with general purpose models unless we see some genre specific fine tunes.

Writing has an audience, purpose, constraints, and desired outcome. Creating tests for writing is more difficult than creating a unit test, but it's not undefinable. It's just engineering.

I disagree, and not because I think AI can write as good as a good writer does, but because in 90%+ of the cases, _companies won't care_.

I did translation as a side job for about 20 years (helped me keep my language skills sharp, and some side income was welcome). I was good at it. But translation was one of the first professions to fall to AI (even before LLMs but especially since), and I saw increasingly that companies were willing to accept the clearly lower-quality work if it meant saving money. A couple of years ago I stopped doing any translation work altogether.

Sure, whoever is hired to translate Murakami's latest novel will be a human, a good writer first and foremost. But that translation work is a tiny fraction of the overall translation work contracted by companies.

The same thing is going to happen with writing. It won't disappear, but demand will drop by >80%.

Even safer jobs might be the ones don't get automated because nobody really cares about, e.g. fishing lobster on a boat

I appreciate what this piece is trying to do directionally but it shares a logical flaw with lots of other pieces about AI and knowledge work. It assumes that in order to disrupt a particular field, AI needs to print serviceable work unassisted; that it's "vibe-shipping".

AI today is most effective when it's not vibing, but rather copiloting a skilled operator. I don't necessarily want my agent to build an entire system for me, even if it's ultimately the author of almost every line of code; I'm actively making decisions throughout. There's obviously a spectrum here but at most points on the spectrum the amount of assistance available is still a step change in the economics.

So too with writing.

The first rule of accelerating writing with AI is that you're not allowed to use a single word the AI suggests. Even if what the model comes up with is great, better than what you could have done, as soon as the AI suggests it it's poisoned. At least with current models, readers can detect LLM prose in the parts per trillion, and as soon as they do you've lost them.

The second rule of writing with AI is that AI encouragement is toxic. A structural consequence of RL is that models are exquisitely tuned to generate responses that make their users perceive value. We recognize this in a gross sense in "sycophancy", but the problem recurs fractally in at finer-grained levels, where stuff like "this part is really strong" will subtly allow the model to set a course for your writing and you'll confidently ship crap.

With those two rules in mind, models are incredibly valuable for writing, more valuable in my experience than the professional copywriters I've worked with. The trick is to get them to make suggestions at a higher level than just writing alternatives:

* Do the sentences in these paragraphs end with the new idea or information?

* Are the real actors in each sentence the grammatical subjects?

* From paragraph to paragraph is there a clear flow of topics, or are things jumping around?

* Is this piece crudded up with metadiscourse like "it's important to note"?

I've had a stack of notecards for ages that I took down from Joseph Williams "Style: Towards Clarity And Grace", the most programmer-brained writing book ever written, I love it very much. For the past year or so I've been feeding them through GPT and Claude one by one, and it's drastically increased the speed at which I can knock out a completed piece.

I think it's pretty hard to argue that AI isn't going to have an impact on the writing profession. It's just not the most obvious impact everyone assumes it will have, where it, like, writes whole op-eds or whatever. At least not yet.

Go on to Ao3 and a popular fandom, say like A Song of Ice and Fire: 9 out of 10 fanfictions written in the past two years stink to high heavens of AI. 9 out of 10. From some that are obvious AI to even normies, to some that slip one or two ai tropes after a couple of chapters, because the author got lazy and stopped editing carefully.

It's sad, infuriating, discouraging. A deluge of slop drowning the last embers of authentic human creativity.

Brother, AI was already a threat to writing before LLM arrived

I was thinking about this yesterday, while going through some slop documentation generated by deepseek. If you compare claude fable 5, side by side with the deepseek, the differences in prose are glaring. It's not even close. Deepseek prose reads like fragmented shorthand, where claude fable 5 comes fairly close to human, certainly not superior to human quality writing.

I watched a podcast with a cognitive scientist and one of main contributors to the theory of linguistic relativity, Lera Boroditsky.

She said something to the effect that, "in this very moment, we are speaking in ways that were never spoken before. We are saying things that no other person has said before...."

Language models are not sample efficient and cannot adapt to evolving language, unless it's documented in large amounts of examples.

So whatever isn't documented, whatever isn't in the training dataset or the rag corpus, the model will always be incredibly different in expression from humans.

I recently have been trying out frontier models on writing. Just for laughs, to bring an idea I have into the world acting more as director than author. I have a multi agent setup and am getting different models to argue about rating the story for clarity and whether the plot is complete.

I agree with the article. Even the frontier models call out that all the characters tend to sound the same. It also started at some point making huge changes to the core premise, and also adding characters willy nilly. The issues it called out with the plot (the ones it actually consulted me on) also made me realize how terrible of a writer I actually am.

Overall, it has been an interesting experience. I look forward to reading my own book!

I understand a lot better now why people are bemoaning KDP being filled with absolute garbage AI slop.

First, I actually had the author of this post as my distributed systems professor back in college. He's not just a super smart dude on a technical level, but he's probably the only CS professor I've ever had who seems to appreciate writing in itself (which may be evident from his blog). I think the combination of these two qualities puts him in a good spot to make the claims he is.

In terms of what he's presenting here, I'll say this: his line of logic highlighting the "dual-mind problem" of writing seems, to me, like the most compelling aspect of this argument. When I write something (especially to a specific audience, including an individual), I must practice cognitive empathy if I want what I write to be consumed properly and effectively. This is a cold way of putting it, but even in text message responses which span a few words towards family or a friend, I can put quite a bit of thought into how it will be received, how they will read it, interpret it, etc. My cadence in just texting, alone, can shift dramatically from one message to a next based on who I'm sending it to, what I'm trying to convey, etc.

All of that is fine and not hard to understand, but I suppose the interesting part is this: when I'm just trying to get information across (i.e., instructions, directions, etc.), my messages will resemble something written by an AI. It's not enjoyable to consume, but that's not the point. But as soon as I want to add a bit of "fun" to a message, the task I'm performing is completely different. It's no longer just an exchange of information, but it's an attempt to invoke specific feelings, visualizations, memories, etc., in the other person/people.

I do have a hard time imagining what it will take for AIs to be able to do THAT effectively. I think they're fine at conveying information, but I think people are already becoming very aware that conveying information, in itself, is not enough to be effective. These LLMs don't "care" to "entertain" you with what they're writing at you about. They're just spitting out the mathematically derived facts with, seemingly, no meaningful ability to invoke deeper thoughts in their audiences' minds. And that might not seem particularly important outside the context of writing fiction, etc., but I think it's actually pretty critical even in "dry" settings, like explaining code, because the thoughts, feelings, emotions, etc., invoked through reading IS the output of reading (even if it doesn't seem that way).

Perhaps AI will be able to do this more effectively if they're trained on direct brain signals or something. Like, train the AI not just to convey accurate information, but also encourage it to do so in a way which stimulates different areas of the brain with different intensities based on the premise that, doing so, is actually what people are getting out of that text.

> ... and model checkers can instantly catch errors.

Lol. If that were true software would've been a lot better historically... Model checkers don't scale to 90% of the software we write. Typically you have to 1) heavily abstract the program and 2) put it in some sort of harness to specify how you want to model the outside world (which will always fall short of practice). And probably 10 other workarounds since most model checkers are Research Grade Software™. Not saying they're not tremendously useful, but that bullet doesn't hold up at all.

"Humans are going to be significantly better at writing things intended for other humans to consume" is completely at odds with "And organizations care enough about the gap, and are willing to pay for the difference".

No, that's wishful thinking.

This feels premature. The thing about the LLMs people use today is that it's a handful of super expensively trained models serving 1000s of use cases ranging from frontier math to recipe planning.

We get to learn the foibles and language of Claude and ChatGPT as a result. The slop is almost detectable if you provide no steering prompts about story structure, narrative structure, or stylistic cues. And most writers are not finetuning the weights to their LLMs explicitly.

If you invest time into doing all that (not really trivial stuff), the results will be better.

The safest jobs are the ones that can't be done behind a screen. I'm a technician and there's no robot that could replace me and there won't be for a long time.

The reason LLMs use the same cadence and cliches is that their RLHF does not emphasize writing well, the way it emphasizes, say, coding. And the reason for that should be pretty obvious: coding is where the big money is at.

So the author may be correct, but for a different reason: unless writing starts being very valuable as a profession, it's unlikely the labs will spend significant resources making their AI models better at it.

I think it's likely to be lawyers who are the safest. Lawyers are the ones primarily in charge of the government, and I think it's likely that as soon as they feel like the legal profession is threatened, they will pass laws making it illegal to use AI for legal issues. It's not all sunshine and roses (paralegals are probably hosed), but the lawyers seem like they'll do ok.

It’s orating.

Yes write, but then orate.

Look at all of our AI feeds.

They want to be us so bad.

> Since LLMs lack an active mental model of a specific human reader... They cannot empathize with the human reader, as they don't have the human lived experience. And they have zero skin in the game.

LLM is perfectly capable of empathy, it just never told to do so.

Most writers today lack empathy and have no lived experience. Young californian uni graduates have strong opinions on everything, but produce repetitive boring preachy cringe stuff.

I will take well prompted LLM generated writing anytime over thst!

I don't think there's any chance people will be employed to sell the output of their brains in 20 years, outside of "athletes", or whatever we end up calling people who think for sport. We mostly succeeded at doing this with muscles, and today there's no market for hauling rocks out of quarries manually. We're in the process of doing that for brains too.

AI will outdo people in all practical uses. We're already there for debugging and getting very close for coding, and we're in the middle of the largest investment in human history to expand that to everything else.

If we do a good job of alignment, AI will treat people like those cats "in charge" of train stations in Japan: our every need will be accommodated, but we won't be controlling things we don't understand.