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.
LLM prose has degraded with each model update, the models are now being RL'd into wall of texts that only makes sense to other agents. I think its going to become more obvious as we move forward that is llm generated because labs seem to only be focused on tool-calling/agentic-coding environments, as that's the only thing that drives revenue.
There may come players who focus on models that are good at writing for technical writing/docs , copyrighting ect but I think people will lean towards not using them and will rather have the "human touch" for the things that directly impact brand perception.
Keep in mind, every single AI company that is selling the idea that you don't need to hire designers and web design is "solved" have $100k retainer designers crafting their landing pages.
Yeah, this tracks with my experiments. About every 6 months or so for the past year-and-a-half I've tried using the "frontier" LLMs to as-near-as-possible autonomously write novel length stories, because I find it fascinating.
When I had Gemini 2.5 write a novel, it wasn't really objectively "good" by any stretch of the imagination, but while the prose was very purple and full of cliches and, well, bad writing I guess, it still felt ... subjectively good, at least for what it was.
Last week I did a run with GPT-5.6, and wow. On the one hand, it managed to produce 110,000 words that were "shockingly" coherent. The model was able to maintain state and plot lines and background details extremely well, much better than older models.
But I just don't like the prose. I haven't really liked _any_ prose that GPT-5.6 produces. It's significantly better at "instruction following" and keeping track of things, but, wow.
> “The sequence is consistent with their voluntary choices.” Mara enlarged the uncertainty field rather than the result. “It does not prove what happened to anyone we can’t observe. It does not prove contact did this. And it does not turn the Shard into treatment.”
GPT-5.6 in particular becomes so fixated on certain ideas like "consent" and epistemology, that by the end of the narrative, the prose and dialogue are all just "agent speech", despite the prompt/harness specifying that it's a _novel_ with narrative prose and such.
Interestingly, the model itself produces an accurate critique of its own output:
> The draft has become a *consent-centered medical, legal, and logistical procedural*. The important drift is therefore not that many events were omitted. It is that the retained events now prove a different thesis.
Which begs the question of if it would do better with a couple rounds of output -> critique -> revision. But I think I've had enough LLM prose for a bit...
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.
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.
Copywriting struggles with the issue that businesses know they need it, but they don't value it. It's absolutely shocking to see how poorly companies, and governments, communicate and no amount of AI/LLM usage will change it. For decades it's been neglected and relegated to "cover my ass"- writing.
The majority of AI deployment in businesses could have been avoided, if more attention had been placed on good communication. LLMs don't yield better communication or corporate writing, just more of it, because no one in business seems to understand the value or have the ability to recognize good writing.
Equity trading is not zero sum. There are real businesses and assets behind the equities they represent that have actual value and produce actual money.
theoretically equity trading is about distributing stonks most optimally so that productive companies get more and less productive ones get less, improving productivity of society as a whole
Derivatives (options, futures, swaps, etc) are a zero sum market, there is always a winner and a loser necessarily because the contracts have an expiration date.
The same is not true for trading equities, they are not zero sum. There are dividends, buybacks, companies will sometimes spin off a part or parts as separate companies that you get newly issued shares of stock from (GE splitting into parts is a recent example), public companies get taken private at a premium to the market price, etc.
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.
If you're good at it, which extremely few people are. It's my belief that not enough good writing exists for the tech companies to train an LLM. At least not a generally applicable on.
Exactly. And it's not only about whether a specific job is safe from AI, but whether it's safe from the effects of those that aren't safe: competition from laid off people and downward wage pressure. Then there's the backpressure into the educational pipeline.
Similar, much narrower crises were managed for manufacturing jobs during the worldwide industrialization era. The solution for those displaced by trade was retraining.
Unfortunately, at the moment we have no clue of what workers should be retrained to. Not writing.
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".
But 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.
If you thought all of the money being poured into data centers was excessive, wait until people can order up a proto-sentient sex bot to their front door. Oh boy.
"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".
> 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!
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.
Except that in the worst-case AI scenarios where a huge portion of the professionals who work "behind a screen" lose their jobs, the entire labor market will have to deal with that.
There's so much focus in the US on vocations but Americans have no idea how little mechanics, plumbers, etc. in other countries make. There are a variety of reasons for this but one of the biggest is that you can't charge $200/hour to fix a leak when $200 represents a double-digit percentage of a worker's monthly salary.
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.
"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."
Software can be checked for being 'written well' by compilers / linters etc. There is no equivalent for well-written natural prose. Spelling and grammar checkers haven't a clue about prose semantics.
They just haven't got to it yet. It's not a high priority but eventually they will make a solid effort on easier style guidance and more humanization. They will probably drop the most worn ou constructs like "not X, not Y, but Z" that have become tells and target a lot of known issues in reinforcement training.
It's slightly weird how confident writers are that it won't get improved.
No, they use "the same cadence and cliches" because they inflate a short and ambiguous prompt into long and specific prose by making statistical assumptions about what best fills in the gaps. It's not a training problem, it's an information theory problem, and it's not really surmountable.
Any given model will always have some distinct implicit voice that its biased towards for that infill content, and so a popular model will always become exhaustingly common, painfully familiar, and cliche. Users can use more elaborate prompts that shift the voice away from the most normative and towards some other nodes, but they need to put in special effort for that, and what people-at-scale specifically want from these tools is to put in very little effort, so we can expect that overwhelming number of casual and naive users will always be generating cliche slop with them.
Code escapes this problem not because of training but because it specifically benefits from cliche (boilerplate, patterns, etc) and so an model whose code "voice" reflects your own taste as a coder (or your toolchain's taste as a vibecoder) is going to feel like productive output rather than slop. But it's still cliche.
What's interesting is that we still read AI's output and that's still the primary interface between a human and the AI. Implicitly that means that writing is important and I think we're already starting to see this with the complaints around how Claude "talks"
Claude "talk" is particularly annoying if you're using Claude to perform a job function (write code, analyze a spreadsheet, etc.) but average people reading an ad, Facebook post, etc. don't really care from what I can tell. In fact, I think most of them have no idea what Claude "talk" is, even if some of them notice common patterns that have emerged recently.
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.
Unless the model watermarks text, it will become increasingly difficult to tell the difference between human legalese and AI legalese. Especially if they make it illegal, that will create a strong incentive to find-tune to imitate human lawyers more convincingly.
Eh, I think we're going to run into the same issues we're seeing in software. That is, junior and lower status positions (so SDETs in software, paralegals in law) are rapidly being downsized, and that's going to have a big issue in the future where there isn't the same talent funnel. And then in the future those that work their way up will basically be dependent on working with AI.
It is great time to start business as downsized paralegal. While ago there was an app that objected parking ticket fines in NYC. Anyone can find similar niche to specialise in, and just produce boring repetitive law work using LLM templates and mostly automated work.
> I think it's likely to be lawyers who are the safest. Lawyers are the ones primarily in charge of the government
Given what seems like an increasingly inevitable deprecation of these outdated, lumbering nation-states, it seems to me that these two assertions are mutually exclusive.
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.
AI will outdo people in all practical uses. We're already there for debugging and 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.
There may come players who focus on models that are good at writing for technical writing/docs , copyrighting ect but I think people will lean towards not using them and will rather have the "human touch" for the things that directly impact brand perception.
Keep in mind, every single AI company that is selling the idea that you don't need to hire designers and web design is "solved" have $100k retainer designers crafting their landing pages.
When I had Gemini 2.5 write a novel, it wasn't really objectively "good" by any stretch of the imagination, but while the prose was very purple and full of cliches and, well, bad writing I guess, it still felt ... subjectively good, at least for what it was.
Last week I did a run with GPT-5.6, and wow. On the one hand, it managed to produce 110,000 words that were "shockingly" coherent. The model was able to maintain state and plot lines and background details extremely well, much better than older models.
But I just don't like the prose. I haven't really liked _any_ prose that GPT-5.6 produces. It's significantly better at "instruction following" and keeping track of things, but, wow.
> “The sequence is consistent with their voluntary choices.” Mara enlarged the uncertainty field rather than the result. “It does not prove what happened to anyone we can’t observe. It does not prove contact did this. And it does not turn the Shard into treatment.”
GPT-5.6 in particular becomes so fixated on certain ideas like "consent" and epistemology, that by the end of the narrative, the prose and dialogue are all just "agent speech", despite the prompt/harness specifying that it's a _novel_ with narrative prose and such.
Interestingly, the model itself produces an accurate critique of its own output:
> The draft has become a *consent-centered medical, legal, and logistical procedural*. The important drift is therefore not that many events were omitted. It is that the retained events now prove a different thesis.
Which begs the question of if it would do better with a couple rounds of output -> critique -> revision. But I think I've had enough LLM prose for a bit...
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.
The majority of AI deployment in businesses could have been avoided, if more attention had been placed on good communication. LLMs don't yield better communication or corporate writing, just more of it, because no one in business seems to understand the value or have the ability to recognize good writing.
The same is not true for trading equities, they are not zero sum. There are dividends, buybacks, companies will sometimes spin off a part or parts as separate companies that you get newly issued shares of stock from (GE splitting into parts is a recent example), public companies get taken private at a premium to the market price, etc.
Same with code, but that doesn't seem to matter anymore.
From what I heard it's a bloodbath at the bottom.
Similar, much narrower crises were managed for manufacturing jobs during the worldwide industrialization era. The solution for those displaced by trade was retraining.
Unfortunately, at the moment we have no clue of what workers should be retrained to. Not writing.
But 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.
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!
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.
How long after that moment did the ChatGPT robots need to invent the time travel machine which let you come back to 2026 to confidently claim this?
There's so much focus in the US on vocations but Americans have no idea how little mechanics, plumbers, etc. in other countries make. There are a variety of reasons for this but one of the biggest is that you can't charge $200/hour to fix a leak when $200 represents a double-digit percentage of a worker's monthly salary.
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.
Software can be checked for being 'written well' by compilers / linters etc. There is no equivalent for well-written natural prose. Spelling and grammar checkers haven't a clue about prose semantics.
It's slightly weird how confident writers are that it won't get improved.
Any given model will always have some distinct implicit voice that its biased towards for that infill content, and so a popular model will always become exhaustingly common, painfully familiar, and cliche. Users can use more elaborate prompts that shift the voice away from the most normative and towards some other nodes, but they need to put in special effort for that, and what people-at-scale specifically want from these tools is to put in very little effort, so we can expect that overwhelming number of casual and naive users will always be generating cliche slop with them.
Code escapes this problem not because of training but because it specifically benefits from cliche (boilerplate, patterns, etc) and so an model whose code "voice" reflects your own taste as a coder (or your toolchain's taste as a vibecoder) is going to feel like productive output rather than slop. But it's still cliche.
There will always be people that work against their own profession and colleagues for short term gains
If you are taking steps to protect your brain maybe in a way you are also protecting your job?
Who knows how this all turns out years from now.
Given what seems like an increasingly inevitable deprecation of these outdated, lumbering nation-states, it seems to me that these two assertions are mutually exclusive.
Yes write, but then orate.
Look at all of our AI feeds.
They want to be us so bad.
AI will outdo people in all practical uses. We're already there for debugging and 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.