The safest job from AI may be writing(muratbuffalo.blogspot.com) |
The safest job from AI may be writing(muratbuffalo.blogspot.com) |
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.
What makes you think that there are economic incentives to build an AI that has the same capabilities as the human brain?
The current scaling story is to intentionally take a system that objectively doesn't and sell it as if it does.
I'm not cocky enough to bet against it. Especially since now AI is solving frontier math problems, debugging better than humans, and writing most of the content posted to Hacker News.
One year ago, the writer Mark Lawrence posted an article [0] (featured on HN) describing how he generated 4 pieces of flash fiction with ChatGPT, asked 4 published authors (with a combined book sales of $15M) to also write a flash fiction piece, and then did a blind test asking respondents, for reach of the 8 pieces, (a) whether they thought it was written by a person or not and (b) an overall quality score. The result was that people (who were avid readers and skewed anti-AI) were no better than a coin toss at determining whether the piece was written by ChatGPT, and slightly preferred ChatGPT-written writing overall.
[0]: https://www.marklawrence.buzz/2025/08/the-ai-vs-authors-resu...
However, once it's understood that it wasn't written by a human it loses all value. I use this quote by Kane Parsons a lot but I think it summarizes why humans like art.
> If I see an element of the environment has been, you know they used generative fill or whatever to change something about the scene, even if--. It just shuts the part of my brain that wants to know more about that world and wants to look for details, 'cause I would assume if they're willing to make an arbitrary choice there, they can make an arbitrary choice with literally anything.
Specifically, people like dissecting art whether its writing, a movie or an image. It could be seeing the Chekov's Gun finally fire, and going back through the pages to see the author wrote it in there earlier and you missed it. It could be speculating about the unopened questions the author deliberately left there. This is how a lot of people at least in my circles engage with art and AI provides none of that. Even if the author specifically prompts that into it, those arbitrary choices are going to be made in certain spots that just disinterests me.
1: https://www.sciencedirect.com/science/article/pii/S096969892... 2: https://www.nature.com/articles/s41598-023-45202-3
Hmm...No. I actually enjoy generating short stories for myself - after light editing they are great; they are enjoyable irrespective of the provenance.
> I am appalled at the absolute shit [LLMs] spew as prose. They always follow the same robotic cadence and cliches, and sprinkle the same tired vocabulary all around.
This claim is demonstrably untrue, and central to the author's overall thesis.
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.
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.
Everybody treats this as something innevitable, like the movement of the tectonical plates.
Everybody knows this is being paid with the giant transfer of wealth from the working classes towards the asset owning class via profilgate government expense, ZIRP and QE policies from the FED and the covid so-called stymulus. This created, via Cantillon effect, inflation for consumers and a bizarre and absolutely abnormal deluge of capital for our financial system masters that let they play God and make us, the plebe, obsolete. And yet we don't question anything.
We are accepting as inevitable and uncontrollable something that only looks to be like that.
I know, we all have our lives, out of the confortable anonymate here, I am a enthusiastic AI Booster. Out of some true zealots, we are all hedging our bets to stay on the right side of the city walls of the new techno-feudalism. But, realistically, how many of us the Musks and Thiels will need after they obsolete most of us, no matter how much skills and knowledge anyone of us may have? Just betting to be on the side of the oppresors is a very long bet my friends.
Nothing about it is inevitable, nothing about it requires, as if it was a law of nature, to be unregulated and the people be damned.
Not so many centuries ago, we had peasants riotting and guillotining their opressors, we had proletarian revolutions. They didn't have encryption, the internet, cell phones, digital radio modes that allow you to talk to some other partisan group accross the globe using a cheap chinese SDR and a simple dipole hang between trees.
Things can be different. We still can force some democracy down their throats.
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 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.
In that respect, I'd be more than happy to read more direct quotes from Gemini and then read what a live hacker's take is on it. Slop provides the building blocks, and humans are the alchemists. Need both.
https://hn.algolia.com/?dateRange=pastMonth&page=0&prefix=tr... (not many returns for the last month, most are from me)
This doesn't ring true to me.
LLM prose can be greatly improved with the right prompts.
Now, prose with the right prompts may well still not be as good as a good human writer would write – but it is a lot better than what LLMs produce by default.
If the model can perform better with the right prompts, it suggest they haven't actually done everything they could in the post-training to maximise writing quality.
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.
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.
It's sad, infuriating, discouraging. A deluge of slop drowning the last embers of authentic human creativity.
Eh.
You're waxing poetic about some sort of 'authentic' human craft, but I'm not convinced that's what most people try for when they write fanfiction. Is it possible that some or even most people write fanfiction to create the stories they want to see, and the craft of writing is just a way of achieving their goal?
Yes write, but then orate.
Look at all of our AI feeds.
They want to be us so bad.
A lot of that writing is really bad because of the AI smell, the clunky structure and cliché-ridden awfulness, but strangely some people respond positively to it, because it feels like it might be good writing: it's dramatic, it's got the "AI mic drop", it's got the "this is not X it's Y" and it combines meta-narrative about what it's going to say with ham-fisted attempts to say it.
The net result is that it draws people through the piece by the hand, and if you ignore the AI clichés - or are not familiar with them - you leave feeling like you've read something consequential.
And in some cases, the people who use AI to write for them are surprised if people don't like it, because from their point of view it does the job they wanted it to do, which is to show off whatever it is they are working on.
For a lot of content, that's fine. For people prompt-building something it's a quick and easy "show this off" that is as quick and easy as the prompt they used to build it. Maybe they are iterating ideas and want fast feedback. That's fine - but the "writing" that results is no more valuable that the thing they have "built", because anyone could prompt the same result.
And for a lot of other content, the writing really doesn't need to do anything more sophisticated than fill out a page - it's just eyeballs-for-ads arbitrage, so if you can speed up the process of having something you can drive eyeballs to, and make that cheaper, it probably works.
A lot of writing online was like that before AI came along - there were content farms "spinning" pieces of content from elsewhere, rewriting it, rephrasing it, padding it. Upwork and Fiverr and other marketplaces had loads of "SEO writers" where you could give them sources and they would create five "respins" of everything they published, so that you could feed your low value blogspam farm.
Even a lot of branded editorial publications did this - they would "respin" secondary reporting. A while back I worked on a brand monitoring tool, and it was always interesting to look at the editorial spread - someone published something, another few publications picked it up and rewrote it very quickly, then a whole forest of pieces written from those dripped out over hours or days. If you could land coverage with one of those "ignition" publications you didn't need to promote much further, because there was a natural ecosystem of editorial content farms that would pick up, rewrite, paraphrase and publish.
But that's not "writing" in the sense of something that has been crafted with care for someone else to read, consider and carry away with them. I suspect that's also why AI writes so badly though, because it was trained on human-generated content slop, and on "editorial content" created by people who don't know - and don't care - what good writing looks like. Which, I guess, is why the ingestion of books is now becoming so important. If you train on blogs and reddit comments, the output will resemble blogs and reddit comments.
I wrote about this in another comment here recently - how a fair proportion of people don't know good writing, and how a lot of people don't have the reading sophistication to be able to distinguish between "good" writing and "bad" writing. And in fact, a lot of people find "good" sophisticated writing too hard to understand, so don't read it.
AI has already filled the low value writing gap.
Where it probably won't compete - for now - is editorial sensibility in writing, and editorial voice. The thing that makes someone say "that's AI bullshit" or "there's an inconsistency of ideas in this piece" or "what is the actual story here". From a code perspective, you can compare that to architecture vs code monkeying. Editorial sensibility is architecture, the actual writing - putting of words on the page - is code monkeying. And, like code, the actual writing doesn't necessarily take much time or effort, but the editorial pruning and finessing does.
Good writing is about being able to read, research, digest and have ideas - and make diverse connections between things that might not look the same. But that's the same as great software architecture, which is about "what does this actually need to do, and how are people going to use it". And it's about being able to read what you have written, critically appraise it, and ruthlessly edit, cut, and rewrite.
Good writing starts with "what is the message, why does this matter, and what broader context do my readers need to understand why they should care?"
And when someone is showing off something they have built, or trying to communicate complex ideas in a coherent way, that's where I think AI falls down, because however many words the AI writes, and however convincing they look on the page, unless the human has spent time working with the text it's probably not worth reading.
I write a lot, and after playing around with AI I quickly realised that none of the big AI companies have tools that would allow me to explain a thing I want to write, click "go" and sit back with my feet up. What AI is very good at, though, is as an editorial rubber duck, where I can discuss my ideas with AI, create structure, create bullets and article plans that I - an actual human - can then write from. That helps me a lot when I want to explain complex things, or draw threads from several different disparate things into one coherent argument.
I see AI as a way to get to what I want to write more effectively - but I gave up trying to get the AI to write for me, in my tone of voice, a long time ago.
Maybe that will change - but I can't see a world where I would allow AI to write in my tone of voice without applying significant editorial oversight of my own to what it is generating. And that's what takes the time. Would I allow AI to replace that, even if it could write properly? Probably not.
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.
But these publishers don't get punished because they keep licensing popular foreign franchises, which aren't quite fungible, so consumers don't want to miss out and thus don't vote with their wallets.
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.
The first careers I witnessed being negatively impacted by AI coming onto the scene .. just a couple years ago .. was tech writers. Source: my personal network of tech writer colleagues.
Either your colleagues are facing stiffer competition off-shore (not LLM driven) or their jobs were more about organizing, checking and sorting topics and data (LLMs can take that away)
>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.
Are you joking? The only people I see using generative AI are either companies, shitposters, and spammers. Who are all these people who have stopped paying commissions and moved over to prompting AIs?
Especially on forums like HN, the AI isn't generating the person's views - it's helping them say that what they already mean more clearly in English.
Harness issue, just explicitly tell them to maintain a vocabulary as they go for proper nouns and novel terms.
Second, you vastly overestimate the desire that book publishers have to pay for good translations. I've seen books from reputable European publishers that were (badly) machine-translated by a contracted translator keen to dig their own grave. Yes, Harry Potter will get a good translation, but 1,000 lesser books won't.
1) the privilege of the rich and secure who can do it despite the lack of financials.
2) the ultra passionate who spend every waking moment outside of their day job engaging in the craft. Often to the detriment to their health and social obligations.
You shouldn't need to go all in on a skill just for a chance to maybe one day earn money from it.
The replacement is not better, but it’s good enough for the undiscerning people who prefer the cost pr convenience of LLM-generated content.
In any case, you find yourself competing with oligopolies that can insert their bad LLM output in front of your good work. It’s not a fair fight.
The show will go on and humanity will probably split into those who work deeply with AI and create an AI economy, where AIs develop and interact with each other (much like cloud systems do today) and a human world where people compete with other people both in art and in sports with the best receiving rewards and recognition from other humans.
But AI cannot (yet?) do the job of a skilled writer, coder, graphical artist and musician.
And it is the same problem every time, LLMs lack intention. That's essentially what the article is about, writers choose every word because they have something to convey. Something complex, deliberate, that can't fit in a simple prompt, a LLM can't get these nuances, it is all in the writer's head, so you get something generic, the information to do it better simply isn't there.
But it apply to other arts as well, ChatGPT flyers, Suno music, etc.. they all look and sound the same, because there is no intention behind them, besides all the technical issues, like inconsistent images and instruments blending into each others, the model just can't work with information it doesn't have, so it just generate something generic that looks like its training dataset.
And it is the same with code. Coding is not about the programming language, it is about expressing with precision what the machine has to do, and programming languages are really good at that, that's why they exist. LLMs let you use English instead, but it doesn't change the fact that everything has to be intentional, otherwise, the LLM will just put something that may or may not be what you want.
Most success stories of LLM coding are ports. Because someone already did most of the job with the original code, sometime even twice if we count the tests.
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.
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.
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.
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.
AI doesn't need to write better than the best humans. It only needs to be good enough to replace a large part of the mundane paid writing that used to support writers while they developed their craft.
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%.
Same with code, but that doesn't seem to matter anymore.
Code's ultimate goal, on the other hand, is to be run by computers.
Are we at the point in the hype cycle to go "there's a skill for that" yet?
This would mean the specificity of details, thoughts and sensations as filtered through a specific individual.
There is a theory that what we perceive as beautiful in a face is that every feature, distance between the eyes, width of nose, distance between cheekbones etc is average. Note that a random face is very unlikely to have every feature be average.
With AI writing, the problem is the reverse. LLMs have captured lived experience, but it's the smooshed over experiences of millions of people.
It's not able to create a unique viewpoint which is believable. What comes out are average thoughts, sensations and details, but good writing lives in the specific.
AI's problem isn't that it can't generate unusual things. It's that it struggles to generate believable conjunctions of ordinary things.
But even this is too reductive. AIs don't have a body or a 'soul.' They don't know what it feels like to be a person, so they can't write like one.
Every story boils down to the same predictable elements, even so-called twists are just repeats of the same old twists. Every story is boy-meets-girl, the friendly character at the start is the real villain, it was all just a dream, the power of friendship, etc.
I just asked GPT5 medium for something along those lines and got (condensed): humanity discovers a phenomenon in that the universe has started compressing causality, things start disappearing because they did not have a large enough influence upon the universe - someone's childhood hobby, a friendship that was tenuous at best and their journey is how to deal with (and ultimately mitigate) this new feature of physics.
I remember, when I used ChatGPT the first time to write some E-mails and other documents, I suddenly realized that I sound exactly like corporate communications or most tech writers. That made me realize that these guys are also working off templates and basically produce variations of the same thing. Same as developers do.
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.
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.
This is only if the output is fully compressed. Writing is not just about encoding the writer's ideas but also about how the reader will ingest those ideas. The writer needs to consider when to put in rests in between complex ideas to help the reader flow through the text. This suggests the LLM could be prompted by a dense complex idea to be presented with the boilerplate needed for the human mind read smoothly and without unnecessary effort.
Doesn’t stop people trying.
I think for this argument to be true, the axiom that supports it is that the models have just as much context as they will ever have, and you cannot see being able to give them more / enough to be able to understand your perspective. That feels unlikely to be a position that doesn't change. As a society we're giving more and more context each day to this, and that makes this a valid opinion now, but one that erodes over time.
also were seeing models become worse at writing as they get smarter.
Call it "intention", call it "understanding", call it "effective communication." The lack thereof is obvious and easily identified.
> Most success stories of LLM coding are ports. Because someone already did most of the job with the original code, sometime even twice if we count the tests.
Another way to phrase this is:
Because someone already thought about the problem and what
needs to exist in order to solve it."Give me a poster for my band playing a gig"
Versus
"Give me a poster for my band playing a gig, it should have x y and z. Use a x' artistic style and include elements of y'. The layout should be z'..."
You ask for the default, you get the default.
AI might be able to help someone with vision who lacks proficiency with the tools, but I suspect that's a small minority of people.
AI can help with the execution, and for an amateur, this is important. But for real pros, the ones who actually know the appropriate style and what the layout should be, they usually have the execution nailed down. Prompting will likely drag them down, if they use AI, it is more likely to be in the form of "augmenter" tools: upscaling, content-aware fill, etc...
Again, same idea with code. Programming languages are only an issue for inexperienced coders, for those who really know what they are doing, it is usually second nature, and they may be better served writing the code themselves than trying to get a LLM to do it. Again, AI has its use: completion, analysis, etc...
That's also the reason why I believe that so many people are missing the point with generative AI taking jobs. Because they are not in the field, they only see the execution, image editing software, DAWs and programming languages look arcane, they think it is what takes the most skills, and that if you can get over it using AI, you can do just as well. The truth is that it is not the case, not by a long shot, execution is just the first step.
It is a problem for juniors however. Juniors are at the first step, they have the execution but not much more, they need to grow real skills, but how will they grow these skills if no one want them because they are at the level where AI can do most of their job.
Well, there have been hugos/nebulas awarded to "creative workshop" quality work before (and i mean before LLMs). But that doesn't make those books "epic".
This is similar to observations that effect of AI on quality of apps in stores is mostly non-existent.
In short, the expected effect of AI will be more stuff, faster, not better. Are we seeing that? I think so.
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.
But this is an old trend with technological developments. Thanks to e-mail, most people handle much or all of their own correspondence. There used to be specialized staff for that. They were very good at it! But they were shown the door once they ceased to be strictly necessary, and now we all waste a bunch of time fiddling with Outlook instead of doing our actual work.
Thank you for saying the first part. It truly boggles the mind to see how badly most organizations communicate, even those with professional staff tasked with communications.
Regarding the second part: AI/LLM usage will lead to improvements for some people/organizations, if only for the simple reason that it removes barriers to communications. All of a sudden city hall or a third-tier supplier can quickly update their websites in multiple languages, process email communications more quickly, and empower staff who weren't good writers.
People can tell when something they are experts in is being done poorly, but others can't, and it frustrates the experts. Then, those same people think something completely different is being done well despite what experts in that respective field say. It's like when someone from your family reads an article about your profession and then proceeds to tell you how your job works. lol
It's a really pervasive issue in society IMO. And a few prompts in someone's favorite LLM just reinforces it to people that don't know (any better|what they don't know).
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...
Have you tested this systematically, or is it possible that you are experiencing survivorship bias? If there were any generative art pieces that you didn't notice, you would have thought that they were human-made. Therefore, all the pieces you identified were "obvious" to you. Not to mention false positives.
We can replace their statement by "I'm a visual artist and at generative art is often blindingly obvious.... To me....". In other words, they can notice some or most generative art but other people familiar with art can't, or at least can't as often as they do. I think what they are trying to say isn't too changed by that.
I have made digital art for 30 years and this all just sounds like what people use to say about digital art in general.
The main problem I see with generative art is not that you can tell it is generative. It is that most the art is shit. The same way if you gave a 1000 random people a blank canvas and paint, most the paintings would be shit too.
The counter example is there is a billboard that I see driving sometimes that is obviously AI generated graphics. It is so eye catching compared to any of the other billboards because most billboards are boring.
You are just puppeting the standard gate keeping bullshit to a new art form and personally I sick of reading this.
Who the fuck are you to say what art is or what art can be?
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.
Even if LLM output has to largely follow some statistical rules, yet, first of all, some amount of randomness is normally injected during token generation, and, secondly same true for human speech.
> 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.
This is not true, as LLM has internal knowledge storet in its weight. Unless you force it to produce 2000 words doc out of 3 word prompt, you would end up adding some sense information.
>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.
True, here I agree with you. But using finetuned or simply less popular models like Kimi, Hy etc. should take care of that.
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.
For now. It also seems like we're still paying software engineers.
Was a beautiful house, which I briefly lived in during scatterbrained renovations (never met the new owner).
The bank forclosed on his house ~late2024/early2025... anybody can now just use an LLM to translate (in real time), on sub-$300 hardware (offline, open-source models run via e.g: Whisper[GUI]).
Yes, I covered that under "formulaic content". As I've already said, a lot of this was already done decades ago. East Asian manuals and copies using machine translation are kind of a meme. Human translators are not losing any work from this. These are all businesses that were never going to employ a translator to begin with; it was either machine translation or nothing.
>Second, you vastly overestimate the desire that book publishers have to pay for good translations. I've seen books from reputable European publishers that were (badly) machine-translated by a contracted translator keen to dig their own grave. Yes, Harry Potter will get a good translation, but 1,000 lesser books won't.
Well, you're not citing any examples, so you you don't give me much to work with. I haven't read a translated book in ages (I don't think), but I do move in circles where translations, human- and machine-made, are freely distributed, and people kick up a fuss over machine translations that they didn't even have to pay for. I can't imagine a paying reader being any kinder upon realizing that they're traded their hard-earned money for machine-produced nonsense. You yourself admit that the translator who does this is digging their own grave. How is this not a problem that corrects itself?
Thing is, LLMs don't seem to be the right vehicle for AGI, but even if the nerds understand that, see above about profit perspectives and investors. These companies are under insane pressure to deliver the next super hit or be seen trying.
The company I work for also encourages us to use agents as much as possible. Now, I spend more time in the terminal than in VS Code. However, the situation is different for those who work in regulated areas.
For example the recent Skild AI fairly general imitation capability. It really is a huge amount of effort on increasing the generality of humanoid robots going on now and a lot of demonstrations coming out showing off progress.
With the current trajectory there is no reason to think it will stop soon. There is a lot of motivation to innovate and train and it is pushing things forward.
If you're not looking for evidence of improvement, or rather looking for failure cases, then you're not going to see that trajectory.
I assume you meant "now" instead of "not": "The person who used to edit things for me has now been replaced,"
That's the part I question. You have to be a really good communicator to put yourself in the place for the reader and adjust your language to your target audience. So much of "professional" communication fails because the language assumes that the recipient is knowledgeable or even just interested. If you can't communicate effectively on your own, I doubt that you can prompt the LLM to do it for you. It's going to be very much like development, if you're a skilled developer, LLMs are a massive boost to your productivity, but if you can't code, you're just producing garbage.
AI is going to produce an increase in communication, but that is not what we need. We need better and more targeted communication. AI doesn't need to help with the targeting, we can already do that, and do it cheaper than an LLM, and again, I don't believe the majority of people are able to prompt the LLM to generate better communication, because they don't know what that looks like.
>a limited amount of diplomatic communiques
With zero data to support it, I'd bet good money the vast majority of semi-professional translators (that is, those not employed by publishers but still making some money off of their work) work on fiction, translating comics, subtitles, etc.
Actually, come to think of it, mixed media like those will be the last where machine translation will be able to fully take over humans, just because the text doesn't contain the entire relevant context for the job.
Full time professional, 20 years of experience.
Keep in mind that 99% of translators are freelance. Translating something takes only a fraction of the time it takes to write the original, so publishers only have full time project managers and editors and hire translators per job as needed.
Engines like Deepl do give very good results on some single sentences. After all, they are huge databases of previous human translations, they are bound to nail it here and there. Where it falls apart is in keeping a good average and a consistent voice, so either you leave it all as messy AI sludge or you rewrite it substantially (at which point it's a glorified dictionary)
> The latest models got even worse.
Which models? This is one of those things that likely has both model and domain specific aspects that impact your experience. In my experience with OpenaAI models predominantly (I previously worked there), they've improved significantly over the last 6-12 months. My experience with Claude is worse, but I haven't spent as much time getting into a mechanical sympathy there. They're still not perfect though and I have many steering docs that help avoid the biggest problems in the models I use when generating docs.
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 will buy y tons of corn from you in April for £x" - now I don't have to worry about how much my corn will cost and you don't have to worry about what your income will be.
I've used some Opus 4.5/4.6 via Antigravity and Sonnet by the web chat. I'm torn because as far as LLMs go, it does feel more ... "literate".
But maybe too literate, judging by how many people are complaining about "Claudeisms". I suspect Claude would be just as susceptible, if not more, to the sort of ... "moralizing" that GPT seems to gravitate towards (for lack of a better term).
It's possible to work around by generating short passages at a time with carefully constructed setup. But it's a real pain.
In this case, part of the experiment was to see what "oh-my-pi", a "fat and feature rich" LLM harness, could do when coupled with modern GPT, given a 6k~ word overview of a story, and told to come up with a plan to write/review/audit it, making use of subagents and all the fun new groovy LLMisms...
Part of the problem was just "it was basing its style off the last scene/chapter", but part of it was also that its instructions were constantly being "compressed" through repeated compactions. Even with the use of subagents, the "top level" agent's prompt was getting muddied, and in the "review" phase, it began to focus more and more on creating increasingly complex ledgers.
You can see this happen in the "plan" files it created for each chapter, looking at word count:
1304 d1-ch-01.md
3701 d1-ch-02.md
5151 d1-ch-03.md
6462 d1-ch-04.md
9587 d1-ch-05.md
10605 d1-ch-06.md
This could definitely be fixed, but honestly, I've about had my fill of the "autonomous writing agent" goal. The idea was to make a model that could generate sufficiently interesting stories based on "vague premises" for my personal entertainment, but, "surprise", getting LLMs to actually produce both "new" and "coherent" content beyond what you specify is _hard_.
It seems like you really do need to just stay "in-the-loop" with every scene, and constantly provide correction/feedback, to correct the "semantic drift".
Or, gasp, I could just try writing things by hand again... :-)
This would never work. Anything longer than 1500 words gonna be bad. To get proper quality you should generate piece by piece then stitch.
Though, I'd also agree that if you're not providing feedback between each piece, the result is gonna suck, or at least, it's not really going to be "more than the sum of its prompt".
I experimented with "introducing randomness" in the form of web search + using older LLMs like EleutherAI's GPT-{J,NeoX} to try and inject novelty into the generation, but I never really got that to work either.