I revise my local public transit guide every time I experience a foreign public transit system. I improve my writing by walking in my readers' shoes and experiencing their confusion. Empathy is the engine that powers my work.

Most of my information is carefully collected from a network of people I have a good relationship with, and from a large and trusting audience. It took me years to build the infrastructure to surface useful information. AI can only report what someone was bothered to write down, but I actually go out in the real world and ask questions.

I have built tools to collect people's experience at the immigration office. I have had many conversations with lawyers and other experts. I have interviewed hundreds of my readers. I have put a lot of information on the internet for the first time. AI writing is only as good as the data it feeds on. I hunt for my own data.

People who think that AI can do this and the other things have an almost insulting understanding of the jobs they are trying to replace.

You mention you've done work for public transit - well, if public transit documentation suddenly starts being terrible, will it lead to an immediate, noticeable drop in revenue? Doubt it. Firing the technical writer however has an immediate and quantifiable effect on the budget.

Apply the same for software (have you seen how bad tech is lately?) or basically any kind of vertical with a nontrivial barrier to entry where someone can't just say "this sucks and I'm gonna build a better one in a weekend".

I don't work for the public transit company; I introduce immigrants to Berlin's public transit. To answer to the broader question, good documentation is one of the many little things that affect how you feel about a company. The BVG clearly cares about that, because their marketing department is famously competent. Good documentation also means that fewer people will queue at their service centre and waste an employee's time. Documentation is the cheaper form of customer service.

Besides, how people feels about the public transit company does matter, because their funding is partly a political question. No one will come to defend a much-hated, customer-hostile service.

Sure, the megacorps may start rotting from the inside out, but we already see a retrenchment to smaller private communities, and if more of the benefits of the big platforms trickle down, why wouldn’t that continue?

Nicbou, do you see AI as increasing your personal output? If it lets enthusiastic individuals get more leverage on good causes then I still have hope.

When it became cheaper to make games did the quality go up?

When it became cheaper to mass produce X (sneakers, tshirts, anything really) did the quality go up?

It's a world that is made of an abundance of trash. The volume of low quality production saturates the market and drowns out whatever high quality things still remain. In such a world you're just better of reallocating your resources from the production quality towards the the shouting match of marketing and try to win by finding ways to be more visible than the others. (SEO hacking etc shenanigans)

When you drive down the cost of doing something to zero you you also effectively destroy the economy based around that thing. Like online print, basically nobody can make a living with focusing on publishing news or articles but alternative revenue streams (ads) are needed. Same for games too.

No, but the availability (more people can afford it) and diversity (different needs are met) increased. I would say that's a positive. Some of the expensive "legacy" things still exist and people pay for it (e.g. newspapers / professional journalism).

Of course low quality stuff increased by a lot and you're right, that leads to problems.

Rather than think in terms of making things cheaper for people to afford we should think how to produce wealthier people who could afford better than the cheapest of cheapest crap.

> Rather than think in terms of making things cheaper for people to afford we should think how to produce wealthier people who could afford better than the cheapest of cheapest crap.

This problem actually runs deep and is systemic. I am genuinely not sure how one can do it when the basis of wealth derives from what exactly? The growth of stock markets which people call bubbles or the US debt crisis which is fueling up in recent years to basically fuel the consumerism spree itself. I am not sure.

If you were to make people wealthy, they might still buy cheapest of cheapest crap just at a 10x more magnitude in many cases (or atleast that's what I observed US to do with how many people buy and sell usually very simple saas tools at times)

Maybe my opinion is just biased and I'm in the comfortable position to pass judgment but I'd like to believe that more people would be more ethical and conscious about their materialistic needs if things had more value and were better quality and instead of focusing on the "price" as the primary value proposition people were actually able to afford to buy other than the cheapest of things.

Wouldn't the economy also be in much better shape if more people could buy things such as handmade shoes or suits?

I hear ya but I wonder how that reflects on Open source software which was the GP request created by LLM let's say. Yes I know it can have bugs but its free of cost and you can own it and modify it with source code availability and run it on your own hardware

There really isn't much of a difference in terms of hardware/electricity just because of these Open source projects

But probably some for LLM's so its a little tricky but I feel like open source projects/ running far with ideas gets incentivized

Atleast I feel like its one of the more acceptable uses of LLM in so far. Its better because you are open sourcing it for others to run. If someone doesn't want to use it, that's their freedom but you built it for yourself or running with an idea which couldn't have existed if you didn't know the details on implementations or would have taken months or years for 0 gains when now you can do it in less time

It significantly improves to see which ideas would be beneficial or not and I feel like if AI is so worrying then if an idea is good and it can be tested, it can always be rewritten or documented heavily by a human. In fact there are even job posts about slop janitor on linkedin lol

> Wouldn't the economy also be in much better shape if more people could buy things such as handmade shoes or suits?

Yes but also its far from happening and would require a real shake up in all things and its just a dream right now. i agree with ya but its not gonna happen or not something one can change, trust me I tried.

This requires system wide change that one person is very unlikely to bring but I wish you best in your endeavour

But what I can do on a more individualistic freedom level is create open source projects via LLM's if there is a concept I don't know of and then open sourcing it for the general public and if even one to two people find it useful, its all good and I am always experimenting.

I'm not trying to be snarky, but, if the principle is broadly applied, then what is the difference between these two? (I agree that, if it can only be applied to a limited population, making a few poor people wealthier might be better than making a few products cheaper.)

One of the qualia of a product is cost. Another is contemporaneity.

If we put these together, we see a wide array of products which, rather than just being trash, hit a sweet spot for "up-to-date yet didn't break the wallet" and you end up with https://shein.com/

These are not thought of as the same people that subscribe to the Buy It For Life subreddit, but some may use Shein for a club shirt and BIFL for an espresso machine. They make a choice.

What's more, a “Technivorm Moccamaster” costs 10x a “Mr. Coffee” because of the build and repairability, not because of the coffee. (Amazon Basics cost ½ that again.)

Maybe Fashion was the original SEO hack. Whoever came up with the phrase "gone out of style" wrought much of this.

When it became cheaper to mass produce sneakers, tshirts, and anything, the quality of the individual product probably did go down, but more people around the world were able to afford the product, which raised the standard of living for people in the aggregate. Now, if these products were absolute trash, life wouldn't make much sense, but there's a friction point in there between high quality and trash, where things are acceptable and affordable to the many. Making things cheaper isn't a net negative for human progress: hitting that friction point of acceptable affordability helps spread progress more democratically and raise the standard of living.

The question at hand is whether AI can more affordably produce acceptable technical writing, or if it's trash. My own experiences with AI make me think that it won't produce acceptable results, because you never know when AI is lying: catching those errors requires someone who might as well just write the documentation. But, if it could produce truthful technical writing affordably, that would not be a bad thing for humanity.

Today the situation is very different and I'm not quite sure why you compare a time in history where the average person was illiterate and (printed) books were limited to a very small audience who could afford them, with the current era where everybody is exposed to the written word all the time and is even dependent on it, in many cases even dependent on it's accuracy (think public services). The quality of AI writing in some cases is so subpar, it resembles word salad. Example goodreads: the blurb of this book https://www.goodreads.com/book/show/237615295-of-venom-and-v... was so surreal I wrote to the author to correct it (see in comments to the authors own review). It's better now, but it still has mistakes. This is in no way comparable with the pasts goes down a bit this is destroying trust even more than everything else, because it this gets to be the norm for official documents people are going to be hurt.

It introduces a lower barrier to entry, so more low-quality things are also created, but it also increases the quality of the higher-tier as well. It's important to note that in FOSS, we (Or atleast...I) don't generally care who wrote the code, as long as it compiles and isn't malicious. This overlays with the original discussion...If I was paying you to read your posts, I expect them to be hand-written. If I'm paying for software, it better not be AI Slop. If you're offering me something for free, I'm not really in a position to complain about the quality.

It's undeniable that, especially in software, cheaper costs and a lower barrier to get started will bring more great FOSS software. This is like one of the pillars of FOSS, right? That's how we got LetsEncrypt, OpenDNS, etc. It will also 100% bring more slop. Both can be true at the same time.

In a landscape where the market is mostly filled with junk by spending anything on "quality" any commercial product is essentially losing money.

Isn't this the exact point I was making...? I get you're arguing it's only a single factor, but I feel like the point still stands. More hobbyists, less financial constraints

I truly don't see this happening anymore. Maybe it did before?

If there's real competition, maybe this does happen. We don't have it and it'll never last in capitalism since one or a few companies will always win at some point.

If you're a higher tier X, cheaper processes means you'll just enjoy bigger profit margins and eventually decide to start the enshittification phase since you're a monopoly/oligopoly, so why not?

As for FOSS, well, we'll have more crappy AI generated apps that are full of vulnerabilities and will become unmaintainable. We already have hordes of garbage "contributions" to FOSS generated by these AI systems worsening the lives of maintainers.

Is that really higher quality? I reckon it's only higher quantity with more potential to lower quality of even higher-tier software.

Obviously, yes? Maybe not the median or even mean, but peak quality for sure. If you know where to look there are more high-quality takes available now than ever before. (And perhaps more meaningfully, peak quality within your niche subgenre is better than ever).

> When it became cheaper to make games did the quality go up?

Yes? The quality and variety of indie games is amazing these days.

> When it became cheaper to mass produce X (sneakers, tshirts, anything really) did the quality go up?

This is the case where I don’t see a win, and I think it bears further thought; I don’t have a clear explanation. But I note this is the one case where production is not actually democratized. So it kinda doesn’t fit with the digital goods we are discussing.

> basically nobody can make a living with focusing on publishing news or articles

Is this actually true? Substack enables more independent career bloggers than ever before. I would love to see the numbers on professional indie devs. I agree these are very competitive fields, and an individual’s chances of winning are slim, but I suspect there are more professional indie creators than ever before.

What happens when all the engineers left can't figure out something, and they start opening up manuals, and they are also all wrong and trash. And the whole world grinds to a halt because nobody knows anything.

Nowadays the problem is that both technical and legal means are used to prevent adversarial interoperability. It doesn't matter if you (or AI) can write software faster if said software is unable to interface with the thing everyone else uses.

Thank you so much for saying this. Trying to convince anyone of the importance of documentation feels like an uphill battle. Glad to see that I'm not completely crazy.

I'd argue that this started 30 years ago when automated phone trees started replacing the first line of workers and making users figure out how to navigate where they needed to in order to get the service they needed.

I can't remember if chat bots or "knowledge bases" came first, but that was the next step in the "figure it out yourself" attitude corporations adopted (under the guise of empowering users to "self help").

Then we started letting corporations use the "we're just too big to actually have humans deal with things" excuse (eg online moderation, or paid services with basically no support).

And all these companies look at each other to see who can lower the bar next and jump on the bandwagon.

It's one of my "favorite" rants, I guess.

The way I see this next era going is that it's basically going to become exclusively the users' responsibility to figure out how to talk to the bots to solve any issue they have.

Thank you. I love it when someone poetically captures a feeling I’ve been having so succinctly.

It’s almost like they’re a professional writer…

I have exactly 1 guess but am waiting to say it.

Which means I replied to a bot.

I am officially retiring from social media.

And im new to hackernews lol

Exactly. If the AI-made documentation is only 50% of the quality but can be produced for 10% of the price, well, we all know what the "smart" business move is.

AI-made documentation has 0% of the quality.

As the OP pointed, AI can only document things that somebody already wrote down. That's no documentation at all.

The quality of AI-made documentation may be poor, but calling it 0% is just silly.

I'm sure I'm not the only one who was reading about some interesting but flawed system only to discover later that they were talking about MY OWN SOFTWARE!? (only half-joking here)

First, I understand what you're saying and generally agree with it, in the sense that that is how the organization will "experience" it.

However, the answer to "will it lead to a noticeable drop in revenue" is actually yes. The problem is that it won't lead to a traceable drop in revenue. You may see the numbers go down. But the numbers don't come with labels why. You may go out and ask users why they are using your service less, but people are generally very terrible at explaining why they do anything, and few of them will be able to tell you "your documentation is just terrible and everything confuses me". They'll tell you a variety of cognitively available stories, like the place is dirty or crowded or loud or the vending machines are always broken, but they're terrible at identifying the real root causes.

This sort of thing is why not only is everything enshittifying, but even as the entire world enshittifies, everybody's metrics are going up up up. It takes leadership willing to go against the numbers a bit to say, yes, we will be better off in the long term if we provide quality documentation, yes, we will be better off in the long term if we use screws that don't rust after six months, yes, we will be better off in the long term if we don't take the cheapest bidder every single time for every single thing in our product but put a bit of extra money in the right place. Otherwise you just get enshittification-by-numbers until you eventually go under and get outcompeted and can't figure out why because all your numbers just kept going up.

It means you need judgement-based management to be able to over-ride metric-based decisions, at times.

That’s one way to frame it. An other one is, sometime people are stuck in a situation where all options that come to their mind have repulsive consequences.

As always some consequences are deemed more immediate, and other will seem remoter. And often the incentives can be quite at odd between expectations in the short/long terms.

>this sucks and I'm gonna build a better one in a weekend

Hey, this is me looking at the world this morning. Bear with me, the bright new harmonious world should be there on Monday. ;)

Coding is like writing documentation for the computer to read. It is common to say that you should write documentation any idiot can understand, and compared to people, computers really are idiots that do exactly as you say with a complete lack of common sense. Computers understand nothing, so all the understanding has to come from the programmer, which is his actual job.

Just because LLMs can produce grammatically correct sentences doesn't mean they can write proper documentation. In the same way, just because they are able to produce code that compiles doesn't mean they can write the program the user needs.

“Technology needs soul”

I suppose this can be generalized to “__ needs soul”. Eg. Technical writing needs soul, User interfaces need soul, etc. We are seriously discounting the value we receive from embedding a level of humanity into the things we choose (or are forced) to experience.

I completely agree that the ambitions of AI proponents to replace workers is insulting. You hit the nail on the head with pointing out that we simply dont write everything down. And the more common sense / well known something is the less likely it is to be written down, yet the more likely it might be needed by an AI to align itself properly.

Nicely written (which, I guess, is sort of the point).

I'm exploring ways to organize my Obsidian vault such that it can be shared with friends, but not the whole Internet (and its bots). I'm extracting value out the curation I've done, but I'd like to share with others.

Not from a moral perspective of course, but the technical possibility. And the overton window has shifted already so far, the moral aspect might align soon, too.

IMO there is an entirely different problem, that's not going to go away just about ever, but could be solved right now easily. And whatever AI company does so first instantly wipes out all competition:

Accept full responsibility and liability for any damages caused by their model making wrong decisions and either not meeting a minimum quality standard or the agreed upon quality.

You know, just like the human it'd replace.

That's not sufficient, at least from the likes of OpenAI, because, realistically, that's a liability that would go away in bankruptcy. Companies aren't going to want to depend on it. People _might_ take, say, _Microsoft_ up on that, but Microsoft wouldn't offer it.

I call it the banana bread problem.

To curate a list of the best cafés in your city, someone must eventually go out and try a few of them. A human being with taste honed by years of sensory experiences will have to order a coffee, sit down, appreciate the vibe, and taste the banana bread.

At some point, you need someone to go out in the world and feel things. A machine that cannot feel will never be a good curator of human experiences.

Granted, there's lots that's dystopian about that picture, I'm not advocating for it, but it does start to feel like the main value of the "curator" is actually just data capture. Then they put their own subjective take on that data, but I'm not totally convinced that's better than something that could tell me a data-driven story of: "Here are the top three banana breads in the city that customers keep coming back to have a taste orgasm for".

I don't know though, it's a brave new world and I'm skeptical of anyone who thinks they know how all this will play out.

See Duolingo :)

You may enjoy this story about her work:

https://www.folklore.org/Inside_Macintosh.html

As a counterpoint, the very worst "documentation" (scare quotes intended) I've ever seen was when I worked at IBM. We were all required to participate in a corporate training about IBM's Watson coding assistant. (We weren't allowed to use external AIs in our work.)

As an exercise, one of my colleagues asked the coding assistant to write documentation for a Python source file I'd written for the QA team. This code implemented a concept of a "test suite", which was a CSV file listing a collection of "test sets". Each test set was a CSV file listing any number of individual tests.

The code was straightforward, easy to read and well-commented. There was an outer loop to read each line of the test suite and get the filename of a test set, and an inner loop to read each line of the test set and run the test.

The coding assistant hallucinated away the nested loop and just described the outer loop as going through a test suite and running each test.

There were a number of small helper functions with docstrings and comments and type hints. (We type hinted everything and used mypy and other tools to enforce this.)

The assistant wrote its own "documentation" for each of these functions in this form:

"The 'foo' function takes a 'bar' parameter as input and returns a 'baz'"

Dude, anyone reading the code could have told you that!

All of this "documentation" was lumped together in a massive wall of text at the top of the source file. So:

When you're reading the docs, you're not reading the code.

When you're reading the code, you're not reading the docs.

Even worse, whenever someone updates the actual code and its internal documentation, they are unlikely to update the generated "documentation". So it started out bad and would get worse over time.

Note that this Python source file didn't implement an API where an external user might want a concise summary of each API function. It was an internal module where anyone working on it would go to the actual code to understand it.

But if you treat "write documentation" as a box-ticking exercise, a line that needs to turn green on your compliance report, then it can just be whatever.

If you are in charge of a herd of bots that are following a prompt scaffolding in order to automate a work product that meets 90% of the quality of the pure human output you produce, that gives you a starting point with only 10% of the work to be done. I'd hazard a guess that if you spent 6 months crafting a prompt scaffold you could reach 99% of your own quality, with the odd outliers here and there.

The first person or company to do that well then has an automation framework, and they can suddenly achieve 10x or 100x the output with a nominal cost in operating the AI. They can ensure that each and every work product is lovingly finished and artisanally handcrafted , go the extra mile, and maybe reach 8x to 80x output with a QA loss.

In order to do 8-80x one expert's output, you might need to hire a bunch of people to do segmented tasks - some to do interviews, build relationships, the other things that require in person socialization. Or, maybe AI can identify commonalities and do good enough at predicting a plausible enough model that anyone paying for what you do will be satisfied with the 90% as good AI product but without that personal touch, and as soon as an AI centric firm decides to eat your lunch, your human oriented edge is gone. If it comes down to beancounting, AI is going to win.

I don't think there's anything that doesn't require physically interacting with the world that isn't susceptible to significant disruption, from augmentation to outright replacement, depending on the cost of tailoring a model to the tasks.

For valuable enough work, companies will pay the millions to fine-tune frontier models, either through OpenAI or open source options like Kimi or DeepSeek, and those models will give those companies an edge over the competition.

I love human customer service, especially when it's someone who's competent, enjoys what they do, and actually gives a shit. Those people are awesome - but they're not necessary, and the cost of not having them is less than the cost of maintaining a big team of customer service agents. If a vendor tells a big company that they can replace 40k service agents being paid ~$3.2 billion a year with a few datacenters, custom AI models, AI IT and Support staff, and totally automated customer service system for $100 million a year, that might well be worth the reputation hit and savings. None of the AI will be able to match the top 20% of human service agents in the edge cases, and there will be a new set of problems that come from customer and AI conflict, etc.

Even so. If your job depends on processing information - even information in a deeply human, emotional, psychologically nuanced and complex context - it's susceptible to automation, because the ones with the money are happy with "good enough." AI just has to be good enough to make more money than the human work it supplants, and frontier models are far past that threshold.

- Most people don't communicate as thoroughly and complete - written and verbal - as they think they do. Very often there is what I call "assumptive communication". That is, sender's ambiguity that's resolved by the receiver making assumptions about what was REALLY meant. Often, filling in the blanks is easy to do - as it's done all the time - but not always. The resolution doesn't change the fact there was ambiguity at the root.

Next time you're communicating, listen carefully. Make note of how often the other person sends something that could be interpreted differently, how often you assume by using the default of "what they likely meant was..."

- That said, AI might not replace people like you. Or me? But it's an improvement for the majority of people. AI isn't perfect, hardly. But most people don't have the skills a/o willingness to communicate at a level AI can simulate. Improved communication is not easy. People generally want ease and comfort. AI is their answer. They believe you are replaceable because it replaces them and they assume they're good communicators. Classic Dunning-Kruger.

p.s. One of my fave comms' heuristics is from Frank Luntz*:

"It's not what you say, it's what they hear." (<< edit was changing to "say" from "said".)

One of the keys to improved comms is to embrace that clarify and completeness is the sole responsibility of the sender, not the receiver. Some people don't want to hear that, and be accountable, especially then assumption communication is a viable shortcut.

* Note: I'm not a fan of his politics, and perhaps he's not The Source of this heuristic, but read it first in his "Words That Work". The first chapter of "WTW" is evergreen comms gold.

Nonetheless, I live from that work. If you are correct, there's a fair bit of money on the table for you.

Based on what? Your own zero-evidence speculation? How is this anything other than arrogant punting? For sure we know that the point was something other than how fast the author reads compared to an AI, so what are we left with here?

That’s the logical fallacy anyone is going to be pushed to as soon as judging their individual worth in an intrinsically collective endeavor will happen.

People in lowest incomes which would not be able to integrate in society without direct social funds will be seen as parasites by some which are wealthier, just like ultra rich will be considered parasites by less wealthy people.

Your use of the word parasite, especially in the context of TFA, reminds me of the article James Michener wrote for Reader’s Digest in 1972 recounting President Nixon’s trip to China that year. In an anecdote from the end of the trip, Michener explained that Chinese officials gave parting gifts to the American journalists and their coordinating staffs covering the presidential trip. In the case of the radio/TV journalists, those staffs included various audio and video technicians.

As Michener told it, the officials’ gifts to the technicians were unexpectedly valuable and carefully chosen; but, when the newspaper and magazine writers in the group got their official gifts, they turned out to be relatively cheap trinkets. When one writer was bold enough to complain about this apparent disparity, a translator replied that the Chinese highly valued those who held technical skills (especially in view of the radical changes then going on in China’s attempt to rebuild itself).

“So what do you think about writers?” the complainer responded.

To that, the translator said darkly, “We consider writers to be parasites.”

All the more as part of the underlying representation is actually starting from a structuralist analysis. We try to clarify the situation through classes of issues. But then mid journey we see what looks like an easy ride shortcut, where mapping ontological assessment over social forces in interaction is always one step on the side away. Goat scape is nothing new.

So we quickly jump from, what social structures/forces lead to that awful results, to who can be blamed while we continue to let the underlying anthropological issue rules everyone.

The kind of documentation no one reads, that is just here to please some manager, or meet some compliance requirement. These are, unfortunately, the most common kind I see, by volume. Usually, they are named something like QQF-FFT-44388-IssueD.doc and they are completely outdated with regard to the thing they document despite having seen several revisions, as evidenced by the inconsistent style.

Common features are:

- A glossary that describe terms that don't need describing, such as CPU or RAM, but not ambiguous and domain-specific terms, of which there are many

- References to documents you don't have access to

- UML diagrams, not matching the code of course

- Signatures by people who left the project long ago and are nowhere to be seen

- A bunch of screenshots, all with different UIs taken at different stages of development, would be of great value to archeologists

- Wildly inconsistent formatting, some people realize that Word has styles and can generate a table of contents, others don't, and few care

Of course, no one reads them, besides maybe a depressive QA manager.

And LLM are really good in reading your docs to help someone. So I make sure to add more concrete examples into them

But today's AI might do better than the average tech writer. AI might be able to generate reasonably usable, if mediocre, technical documentation based on a halfheartedly updated wiki and the README files and comments scattered in the developers' code base. A lot of projects don't just have poor technical documentation, they have no technical documentation.

AI would do a great job of fixing their writing, but they don't want to use it, because it's not an official part of "the process".

>and comments scattered in the developers' code base

I'm not so sure about this one. Most devs I've worked with don't use comments.

I think everyone on the team should get involved in this kind of feedback because raw first impressions on new content (which you can only experience once, and will be somewhat similar to impatient new users) is super valuable.

I remember as a dev flagging some tech marketing copy aimed at non-devs as confusing and being told by a manager not to give any more feedback like that because I wasn't in marketing... If your own team that's familiar with your product is a little confused, you can probably x10 that confusion for outside users, and multiply that again if a dev is confused by tech content aimed at non-devs.

I find it really common as well that you get non-tech people writing about tech topics for marketing and landing pages, and because they only have a surface level understanding of the the tech the text becomes really vague with little meaning.

And you'll get lots devs and other people on the team agreeing in secret the e.g. the product homepage content isn't great but are scared to say anything because they feel they have to stay inside their bubble and there isn't a culture of sharing feedback like that.

True, but it raises another question, what were your Product Managers doing in the first place if tech writer is finding out about usability problems

But even if a PM cares about UX, they are often not in a good position to spot problems with designs and flows they are closely involved in and intimately familiar with.

Having someone else with a special perspective can be very useful, even if their job provides other beneficial functions, too. Using this "resource" is the job of the PM.

> But even if a PM cares about UX,

How can a PM do their job if they don't *care* about UX?

I mean... I know exactly happens because I've seen it more than once: the product slowly goes to shit. You get a bunch of PMs at various levels of seniority all pursuing separate goals, not collaborating, not actually working together to compose a coherent product; their production teams are actively encouraged to be siloed; features collide and overlap, or worse conflict; every component redefines what a button looks like; bundles bloat; you have three different rendering tools (ok, I've not seen that in practice but it seems to be encouraged by many "best practices") etc etc

I'm just responding to this:

> what were your Product Managers doing in the first place if tech writer is finding out about usability problems

They might very well be doing their job of caring about UX, by using the available expertise to find problems.

It's a bit like saying (forgive the imperfect analogy): what are the developers doing talking about corner cases in the business logic, isn't the PM doing their job?

Yes, they are. They are using the combined expertise in the team.

Let's allow the PMs to rely on the knowledge and insights of other people, shall we? Their job already isn't easy, even (or especially) if they care.

I think I agree, at least in the current state of AI, but can't quite put my finger on what exactly it's missing. I did have some limited success with getting Claude Code to go through tutorials (actually implementing each step as they go), and then having it iterate on the tutorial, but it's definitely not at the level of a human tech writer.

Would you be willing to take a stab at the competencies that a future AI agent would require to be excellent at this (or possibly never achieve)? I mean, TFA talks about "empathy" and emotions and feeling the pain, but I can't help feel that this wording is a bit too magical to be useful.

For tech documentation, I suppose that AI agents would mainly benefit from Skills files managed as part of the tool's repo, and I absolutely do imagine future AI agents being set up (e.g. as part of their AGENTS.md) to propose PRs to these Skills as they use the tools. And I'm wondering whether AI agents might end up with different usability concerns and pain-points from those that we have.

We have to ask AI questions for it to do things. We have to probe it. A human knows things and will probe others, unprompted. It's why we are actually intelligent and the LLM is a word guesser.

Also true that most tech writers are bad. And companies aren't going to spend >$200k/year on a tech writer until they hit tens of millions in revenue. So AI fills the gap.

As a horror story, our docs team didn't understand that having correct installation links should be one of their top priorities. Obviously if a potential customer can't install product, they'd assume it's bs and try to find an alternative. It's so much more important than e.g. grammar in a middle of some guide.

Tech writing seems especially vulnerable to people not really understanding the job (and then devaluing it, because "everybody can write" - which, no, if you'll excuse the slight self-promotion but it saves me repeating myself https://deborahwrites.com/blog/nobody-can-write/)

In my experience, tech writers often contribute to UX and testing (they're often the first user, and thus bug reporter). They're the ones who are going to notice when your API naming conventions are out of whack. They're also the ones writing the quickstart with sales & marketing impact. And then, yes, they're the ones bringing a deep understanding of structure and clarity.

I've tried AI for writing docs. It can be helpful at points, but my goodness I would not want to let anything an AI wrote out the door without heavy editing.

See my other comment - I'm afraid quality only matters if there is healthy competition which isn't the case for many verticals: https://news.ycombinator.com/item?id=46631038

[insert Pawn Stars meme]: "GOOD docs? Sorry, best I can do is 'slightly better than useless.'"

That's fine, though: as long as the AI's output is better than "completely and utterly useless", or even "nonexistent", it'll be an improvement in many places.

Obviously we still need people to oil the machine, but... a person who deeply understands the product, can communicate shortcomings in process or user flows, can quickly and effectively organize their thoughts and communicate them, can navigate up and down abstraction levels and dive into details when necessary - these are the skills LLMs require.

I do not think that these skills are so easily replaced; certainly the machine can do a lot, but if you acquire those skills yourself you shape your brain in a way that is definitely useful to you in many other aspects of life.

In my humble opinion we will be losing that from people, the upscaling of skills will be lost for sure, but the human upscaling is the real loss.

Yep, and reading you will feel less boring.

The uniform style of LLMs gets old fast and I wouldn't be surprised if it were a fundamental flaw due to how they work.

And it's not even sure speed gains from using LLMs make up for the skill loss in the long term.

<list of emoji-labeled bold headers of numbered lists in format <<bolded category> - description>>

Is there anything else I can help you with?

I'll take imperfect ESL writing or imperfect writing in my native language over LLM soup any day.

I thought it was saying "a letter to those who fired tech writers because they were caught using AI," not "a letter to those who fired tech writers to replace them with AI."

The whole article felt imprecise with language. To be honest, it made me feel LESS confident in human writers, not more.

I was having flashbacks to all of the confusing docs I've encountered over the years, tightly controlled by teams of bad writers promoted from random positions within the company, or coming from outside but having a poor understanding of our tech or how to write well.

I'm writing this as someone who majored in English Lit and CS, taught writing to PhD candidates for several years, and maintains most of my own company's documentation.

They have AI finding reasons to reject totally valid requests

They are putting to court that this is a software bug and they should not be liable.

That will be the standard excuse. I hope it does not work.

By whom?

Your expectations aren't the same everybody has.

On one hand, recent models seem to be less useful than the previous generation of them, the scale needed for training improved networks seems to be following the expected quadratic curve, and we don't have more data to train larger models.

On the other hand, many people claim that what tooling integration is the bottleneck, and that the next generation of LLMs are much better than anything we have seen up to now.

If the business can no longer justify 5 engineers, then they might only have 1.

I've always said that we won't need fewer software developers with AI. It's just that each company will require fewer developers but there will be more companies.

IE:

2022: 100 companies employ 10,000 engineers

2026: 1000 companies employ 10,000 engineers

The net result is the same for emplyoment. But because AI makes it that much more efficient, many businesses that weren't financially viable when it needed 100 engineers might become viable with 10 engineers + AI.

Do you not see the logic?

Au contraire. It's not very often that the cost of labor actually drops to anywhere close to zero, but we have some examples. The elevator operator is a prime example. When it was costly to hire an operator we could only hire a few of them. Nowadays anyone who is willing to operate an elevator just has to show up and they automatically get the job.

If 1,000 engineers are worth having around, why not an infinite number of them, just like those working as elevator operators? Again, there is no cost in this hypothetical scenario.

> Cost is not the only driver to demand.

Technically true, but we're not talking about garbage here. Humans are always valuable to some degree, just not necessarily valuable enough when there is a cost to balance. But, again, we're talking about zero cost. I expect you are getting caught up in thinking about scenarios where labor still has a cost, perhaps confusing zero cost with zero payroll?

Five engineers could be turned into maybe two, but probably not less.

It's the 'bus factor' at play. If you still want human approvals on pull requests then If one of those engineers goes on vacation or leaves the company you're stuck with one engineer for a while.

If both leave then you're screwed.

If you're a small startup, then sure there are no rules and it's the wild west. One dev can run the world.

Peak productivity has always been somewhere between 1-3 people, though if any one of those people can't or won't continue working for one reason or another, it's generally game over for the project. So you hire more.

This is why small software startups time and time again manage to run circles around with organizations with much larger budgets. A 10 person game studio like Team Cherry can release smash hit after smash hit, while Ubisoft with 170,000% the personnel count visibly flounders. Imagine doing that in hardware, like if you could just grab some buddies and start a business successfully competing with TSMC out of your garage. That's clearly not possible. But in software, it actually is.

Is the tech writers backlog also seemingly infinite like every tech backlog I've ever seen?

I am not even quite sure I know how to manage a team of more than two programmers right now. Opus 4.5, in the hands of someone who knows what they are doing, can develop software almost as fast as I can write specs and review code. And it's just plain better at writing code than 60% of my graduating class was back in the day. I have banned at least one person from ever writing a commit message or pull request again, because Claude will explain it better.

Now, most people don't know to squeeze that much productivity out of it, most corporate procurement would take 9 months to buy a bucket if it was raining money outside, and it's possible to turn your code into unmaintainable slop at warp speed. And Claude is better at writing code than it is at almost anything else, so the rest of y'all are safe for a while.

But if you think that tech writers, or translators, or software developers are the only people who are going to get hit by waves of downsizing, then you're not paying attention.

Even if the underlying AI tech stalls out hard and permanently in 2026, there's a wave of change coming, and we are not ready. Nothing in our society, economy or politics is ready to deal with what's coming. And that scares me a bit these days.

Only because it has access to vast amount of sample code to draw a re-combine parts. Did You ever considered emerging technologies, like new languages or frameworks that may be a much better suited for You area but they are new, thus there is no codebase for LLM to draw from?

I'm starting to think about a risk of technological stagnation in many areas.

Try it. The pattern matching these things do is unlike anything seen before.

I'm writing a compiler for a language I designed, and LLMs have no trouble writing examples and tests. This is a language with syntax and semantics that does not exist in any training set because I made it up. And here it is, a machine is reading and writing code in this language with little difficulty.

Caveat emptor: it is far from perfect. But so are humans, which is where the training set originated.

> I'm starting to think about a risk of technological stagnation in many areas.

That just does not follow for me. We're in an era where advancements in technology continues to be roughly quadratic [1]. The implication you're giving is that the advancements are a step function that will soon (or has already) hit its final step.

This suggests that you are unfamiliar or unappreciative of how anything progresses, in any domain. Creativity is a function of taking what existed before and making it your own. "Standing on the shoulders of giants", "pulling oneself up by the bootstraps", and all that. None of that is changing just because some parts of it can now be automated.

Stagnation is the very last thing I would bet on. In part because it means a "full reset" and loss of everything, like most apocalyptic story lines. And in part because I choose to remain cautiously optimistic.

[1]: https://ourworldindata.org/technology-long-run

I suspect a lot of folks are asking ChatGPT to summarize it…

I can’t imagine just letting an LLM write an app, server, or documentation package, wholesale and unsupervised, but have found them to be extremely helpful in editing and writing portions of a whole.

The one thing that could be a light in the darkness, is that publishers have already fired all their editors (nothing to do with AI), and the writing out there shows it. This means there’s the possibility that AI could bring back editing.

i wrote a 5 page essay in November. the AI editor had sixty-something recommendations, and i accepted exactly one of them. it was a suggestion to hyphenate the adjectival phrase "25-year-old". i doubt that it had any measurable impact on the effectiveness of the essay.

thing is, i know all the elements of style. i know proper grammar and accepted orthographic conventions. i have read and followed many different style guides. i could best any English teacher at that game. when i violate the principles (and i do it often), i do so deliberately and intentionally. i spent a lot of time going through suggestions that would only genericize my writing. it was a huge waste of my time.

i asked a friend to read it and got some very excellent suggestions: remove a digressive paragraph, rephrase a few things for persuasive effect, and clarify a sentence. i took all of these suggestions, and the essay was markedly improved. i'm skeptical that an LLM will ever have such a grasp of the emotional and persuasive strength of a text to make recommendations like that.

That makes a lot of sense, but right now, the editing seems to be completely absent, and, I suspect, most writers aren’t at your level (I am sure that I’m not).

It may be better than nothing.

But, when we use "AI" acronym, our brains still recognize "intelligence" attribute and tend to perceive LLMs as more powerful than they actually are.

Current models are like trained parrots that can draw colored blocks and insert them into the appropriate slots. Sure, much faster and with incomparably more data. But they're still parrots.

This story and the discussions remind me of reports and articles about the first computers. People were so impressed by the speed of their mathematical calculations that they called them "electronic brains" and considered, even feared, "robot intelligence."

Now we're so impressed by the speed of pattern matching that we called them "artificial intelligence," and we're back to where we are.

Two years ago, I asked chatgpt to rewrite my resume. It looked fantastic at a first sight, then, one week later I re-read it, and feel ashamed to have sent it to some prospective employers. It was full of cringe inducing babble.

You see, for an LLM there are no hierarchies other than what it observed in their training, and even then, applying it in a different context may be tricky for them. Because it can describe hierarchies, relationships by mimicry, but it doesn't actually have a model of them.

Just an example: It may be able to generate text that recognizes that a PhD title is a step above from a Master’s degree, but sometimes it won't be able to translate this fact (instead of the description of this fact) into the subtle differences in attention and emphasis we do in our written text to reflect those real world hierarchies of value. It can repeat the fact to you, can even kind of generalize it, but it won't take a decision based on it.

It can, even more now, get a very close simulation of this, because relative importance of stuff would have been semantically capture, and it is very good at capturing those subtle semantical relationships, but, in linguistic terms, it absolutely sucks at pragmatics.

An example: Let's say in one of your experiences, you improved a model that detected malignancy in a certain kind of tumor images, improving its false negative rate to something like 0.001%, then in the same experience you casually mention that you tied the CEOs toddler tennis shoes once. Given your prompt to write a resume according to the usual resume enhancement formulas, there's a big chance it will emphasize the irrelevant tennis lace tying activity in a ridiculously pompous manner, making it hierarchically equivalent to your model kung-fu accomplishments.

So in the end, you end up with some bizarre stuff that looks like:

"Tied our CEO's toddler tennis shoes, enabling her to raise 20M with minimal equity dilution in our Series B round"

After all, if he didn't feel foolish for it, he wouldn't've held it in his memory, and thus wouldn't've shared it with us.

Who among us hasn't written an angry email, re-(re-)read it, smugly hit send, slept on it, then regretted the sending?

- too many emojis - too many verbose text - they lack the context of what’s important - critical business and historical context are lost - etc..

They used AI to satisfy the short-term gain: “we have documentation”, without fully realising the long-term consequences of low quality. As a result, imo we’ll see the down spiral effects of bugs, low adoption, and unhappy users.

I'm sure their slop looks FAR better than the garbage my coworkers write. I really wish my coworkers would use AI to edit their writing, because then it might actually be comprehensible.

Kudos to all technical writer who made my job as software engineer easier.

When companies view 50% quality at 10% cost as the 'smart' move, they're optimizing for extraction, not value creation.

The glossary at https://founderstowne.com/extraction-terms.html explores these patterns.

If that was more technical tho, like something more similar to technical writing... I would have had Copilot summarise it for me.

You are correct, the future is collaborative with AI, but not everything will still need to be collaborative...

Technical writing, like manuals and whatnots, that is simply akin to a math problem that, post calculator, has always calculated by calculators - even by people who didn't need them.

It will not be better, there is absolutely loss, it will still happen.

One thing to add is that the LLM doesn't know what it can't see. It just amplifies what is there. Assumed knowledge is quite common with developers and their own code. Or the more common "it works on my machine" because something is set outside of the code environment.

Sadly other fields are experiencing the same issue of someone outside their field saying AI can straight up replace them.

What post was that?

I was terrible writer, but we had to write good docs and make it easy for our customers to integrate with our products. So, I prepared the context to our tech writers and they have created nice documentation pages.

The cycle was (reasonably takes 1 week, depending on tech writer workload):

    1. prepare context
    2. create ticket to tech writers, wait until they respond
    3. discuss messaging over the call
    4. couple days later I get first draft
    5. iterate on draft, then finally publish it

    1. I prepare all the context and style guide, then feed them into LLM.
    1.1. context is extracted directly from code by coding agents 
    2. I proofread it and 97% of cases accept it, because it follows the style guide and mostly transforms my context correctly into customer consumable content
    3. Done. less than 20 minutes

People boast about the gains with LLMs all the damn time and I'm sceptical of it all unless I see their inputs.

Always had to contract external people to get stuff done really well. One was a bored CS university professor, another was a CTO in a struggling tiny startup who needed cash.

Technical writing is part of the job of software engineering. Just like “tester” or “DBA”, it was always going to go the way of the dodo.

If you’re a technical writer, now’s the time to reinvent yourself.

You're going to get some text out of a typical engineer, but the writing quality, flow, and fit for the given purpose is not going to come close to someone who does it every day.

Where I work we have professional technical writers and the quality vs your typical SW engineer is night and day. Maybe you got lucky with the rare SW engineer that can technical write.

During the nineties of the last century I was still naive enough to believe that the great improvements in technology, i.e. the widespread availability of powerful word processors and the availability of the Internet for extremely cheap distribution will lead to an improvement in the quality of technical documentation and to easy access to it for everybody.

The reverse has happened, the quality of the technical documentation has become worse and worse, with very rare exceptions, and the access to much of what has remained has become very restricted, either by requiring NDAs or by requiring very high prices (e.g. big annual fees for membership to some industry standards organization).

A likely explanation for the worse and worse technical documentation is a reduction in the number of professional technical writers.

It is very obvious that the current management of most big companies does not understand at all the value of competent technical writers and of good product documentation; not only for their customers and potential customers, but also for their internal R&D teams or customer support teams.

I have worked for several decades at many companies, very big and very small, on several continents, but, unfortunately only at one of them the importance of technical documentation was well understood by the management, therefore the hardware and software developers had an adequate amount of time planned for writing documentation in their schedules for product development. Despite the fact that the project schedules at that company appeared to allocate much more time for "non-productive tasks" like documentation, than in other places, in reality it was there where the R&D projects were completed the fastest and with the least delays over the initially estimated completion time, one important factor being that every developer understood very well what must be done in the future and what has already been done and why.

At best there may have been a doubling of the speed, though something like +50% is much more likely.

Between e.g. 1980 and 1995 the speed of writing documentation has increased much faster than the speed of writing programs has ever increased, due to the generalization of the use of word processors on personal computers, instead of using typewriting machines.

Many software projects might be completed today much faster than in the past only when they do not start from zero, but they are able to reuse various libraries or program components from past projects, so the part that is actually written now is very small. Using an AI coding assistant does exactly the same thing, except that it automates the search through past programs and it also circumvents the copyright barriers that would prevent the reuse of programs in many cases.

This is not writing something new for scratch, but just using an already existing framework, with minor customization for the new project.

Writing an essentially new program, which does something never accomplished before, proceeds barely faster today than what could be done in 1990, with a programming environment like those of Microsoft or Borland C/C++.

In this case it was #3.

That's one of the ways the system autocorrects. A sensational/indignant post attracts upvotes because that's how upvotes work (this is a weakness of the upvoting system), and this triggers an overheated discussion, which trips the flamewar detector which penalizes the post. It's about as simple a feedback mechanism as a thermostat.

That's why it's not uncommon for something to be at #1 and have tons of upvotes and comments, and then suddenly plummet. We do review all the threads that get that particular penalty but sometimes it takes a while.

Edit: ok, I've reviewed it. In this case, the thread is actually pretty good. I'm not sold on the article*, but a good thread is enough to turn off the flamewar penalty in this case, and I've done so.

(* not a judgment about article quality in general, only about how good a fit it is or isn't for HN)

Writers become more productive = less writers needed not 0 but less.

That’s current step. Now if the promise of cursor that capable of Multi week system to be automated completely. All the internal docs become ai driven .

So only exception are external docs . But … if all software is written by machine there are no readers .

This obviously a vector not a current state :( very dark and gloom

The technology is improving rapidly, and even now, with proper context, AI can write technical documentation extremely well. It can include clear examples (and only a very small number of technical writers know how to do that properly), and it can also anticipate and explain potential errors.

If you want to see how well you understand your program or system, try to write about it and teach someone how it works. Nature will show you how sloppy your thinking is.

Someone has to turn off their brain completely and just follow the instructions as-is. Then log the locations where the documentation wasn't clear enough or assumed some knowledge that wasn't given in the docs.

It’s obviously not AI generated but I’m more speaking to the tonality of the latest gpt. It’s now extremely hard to tell the difference.

I believe you but that’s just a gut feeling. I guess the best way to put this is anyone can write what you wrote with AI and claim it wasn’t written by AI.

   The decision to stop hiring technical writers usually feels reasonable at the moment it’s made. It does not feel reckless. It feels modern. Words have become cheap, and documentation looks like words. Faced with new tools that can produce fluent text on demand, it is easy to conclude that documentation is finally solved, or at least solved well enough.

   That conclusion rests on a misunderstanding so basic it’s hard to see once you’ve stepped over it.

   Documentation is not writing. Writing is what remains after something more difficult has already happened. Documentation is the act of deciding what a system actually is, where it breaks, and what a user is allowed to rely on. It is not about describing software at its best, but about constraining the damage it can do at its worst.

   This is why generated documentation feels impressive and unsatisfying at the same time. It speaks with confidence, but never with caution. It fills gaps that should remain visible. It smooths over uncertainty instead of marking it. The result reads well and fails quietly.

   Technical writers exist to make that failure loud early rather than silent later. Their job is not to explain what engineers already know, but to notice what engineers have stopped seeing. They sit at the fault line between intention and behavior, between what the system was designed to do and what it actually does once released into the world. They ask the kinds of questions that slow teams down and prevent larger failures later.

   When that role disappears, nothing dramatic happens. The documentation still exists. In fact, it often looks better than before. But it slowly detaches from reality. Examples become promises. Workarounds become features. Caveats evaporate. Not because anyone chose to remove them, but because no one was responsible for keeping them.

   What replaces responsibility is process. Prompts are refined. Review checklists are added. Output is skimmed rather than owned. And because the text sounds finished, it stops being interrogated. Fluency becomes a substitute for truth.

   Over time, this produces something more dangerous than bad documentation: believable documentation. The kind that invites trust without earning it. The kind that teaches users how the system ought to work, not how it actually does. By the time the mismatch surfaces, it no longer looks like a documentation problem. It looks like a user problem. Or a support problem. Or a legal problem.

   There is a deeper irony here. The organizations that rely most heavily on AI are also the ones that depend most on high-quality documentation. Retrieval pipelines, curated knowledge bases, semantic structure, instruction hierarchies: these systems do not replace technical writing. They consume it. When writers are removed, the context degrades, and the AI built on top of it begins to hallucinate with confidence. This failure is often blamed on the model, but it is really a failure of stewardship.

   Responsibility, meanwhile, does not dissolve. When documentation causes harm, the model will not answer for it. The process will not stand trial. Someone will be asked why no one caught it. At that point, “the AI wrote it” will sound less like innovation and more like abdication.

   Documentation has always been where software becomes accountable. Interfaces can imply. Marketing can persuade. Documentation must commit. It must say what happens when things go wrong, not just when they go right. That commitment requires judgment, and judgment requires the ability to care about consequences.

   This is why the future that works is not one where technical writers are replaced, but one where they are amplified. AI removes the mechanical cost of drafting. It does not remove the need for someone to decide what should be said, what must be warned, and what should remain uncertain. When writers are given tools instead of ultimatums, they move faster not because they write more, but because they spend their time where it matters: deciding what users are allowed to trust.

   Technical writers are not a luxury. They are the last line of defense between a system and the stories it tells about itself. Without them, products do not fall silent. They speak freely, confidently, and incorrectly.

   Language is now abundant.
   Truth is not.

   That difference still matters.

We looked at documentation and thought, Ah yes. Words. And then we looked at AI and thought, Oh wow. It makes words. And then we did what humans always do when two things look vaguely similar: we declared victory and went to lunch.

That’s it. That’s the whole mistake.

Documentation looks like writing the same way a police report looks like justice. The writing is the part you can see. The job is everything that happens before someone dares to put a sentence down and say, “Yes. That. That’s what this thing really does.”

AI can write sentences all day. That’s not the problem. The problem is that documentation is where software stops flirting and starts making promises. And promises are where the lawsuits live.

Here’s the thing nobody wants to admit: technical writers are not paid to write. They are paid to be annoying in very specific, very expensive ways. They ask questions nobody likes. They slow things down. They keep pointing at edge cases like a toddler pointing at a dead bug going, “This too? This too?”

Yes. Especially this too.

When you replaced them with AI, nothing broke. Which is why you think this worked. The docs still shipped. They even looked better. Cleaner. Confident. Calm. That soothing corporate voice that says, “Everything is fine. You are holding it wrong.”

And that’s when the rot set in.

Because AI does not experience dread. It does not wake up at 3 a.m. thinking, “If this sentence is wrong, someone is going to lose a week of their life.” It does not feel that tightening in the chest that tells a human writer, This paragraph is lying by omission.

So it smooths. It resolves. It fills in gaps that should stay jagged. It confidently explains things no one actually understands yet. It does what bad managers do: it mistakes silence for agreement.

Over time, your documentation stops describing reality and starts describing a slightly nicer alternate universe where the product behaves itself and nobody does anything weird.

This is how you get users “misusing” your product in ways your own docs taught them.

Then comes my favorite part.

You notice the AI is hallucinating. So you add tooling. Retrieval. Semantic layers. Prompt rules. Context hygiene. You hire someone with “AI” in their title to fix the hallucinations.

What you are rebuilding, piece by piece, is technical writing. Only now it’s worse, because it’s invisible, fragmented, and no one knows who’s responsible for it.

Context curation is documentation. Instruction hierarchies are documentation. If your AI is dumb, it’s because you fired the people who knew what the truth was supposed to look like.

And don’t worry, accountability did not get automated away while you weren’t looking. When the docs cause real damage, the model will not be present. You cannot subpoena a neural net. You cannot fire a prompt. You will be standing there explaining that “the system generated it,” and everyone will hear exactly what that means.

It means nobody was in charge.

Documentation is where software admits the truth. Not the aspirational truth. The annoying truth. The truth about what breaks, what’s undefined, what’s still half-baked and kind of scary. Marketing can lie. Interfaces can hint. Documentation has to commit.

Commitment requires judgment. Judgment requires caring. Caring is still not in beta.

This is not an anti-AI argument. AI is great. It writes faster than any human alive. It just doesn’t know when to hesitate, when to warn, or when to say, “We don’t actually know yet.” Those are the moments that keep users from getting hurt.

The future that works is painfully obvious. Writers with AI are dangerous in the good way. AI without writers is dangerous in the other way. One produces clarity. The other produces confidence without consent.

Technical writers are not a luxury. They are the people who stop your product from gaslighting its users.

AI can generate language forever.

Truth still needs a human with a little fear in their heart and a pen they’re willing to hesitate with.

Hire them back.

Good human written docs > AI written docs > no docs > bad human written docs

Why?

Because the legal catastrophe that will follow will entertain me so very very much.

I think this is going to be a defining theme this year.

But most people aren't that great at their jobs.

AI can’t generate insights far beyond what it’s trained on.

Their writing will be a different moat.

What if the next version of AI model gets trained on their work ?

Google returns the best result based on both it's calculations, and click history of what clicks were most successful for a search.

LLM's don't really have that same response partially because it's strength is writing one sentence many different ways. The many different ways to write a sentence doesn't mean it's the best way. If it can write deep sentences, keeping a coherent, connected arc through sentences and stories

LLMs' also generally return the "best" answer as the most "common" one, without weight towards outliers as easily that might be the most true, or the best.

The definition of what is "good" and "correct" can also vary quite a bit, especially with writing.

AI can be configured to look for patterns humans might not see, but we also know humans can see things and scenarios that LLM's aren't trained on and can miss getting to.

As we can tell with AI copy, it all starts to sound the same even if it's new. Real writing ages differently. It can be much more of a finger print. This is an area I'm hoping to learn more about from the talented writers in my life - it seems the better the writer, the more they can see the holes of LLM and also be the best power users of LLMs by their superior ability to use words whether they realize it or not.

Why should I hire a dedicated writer if I have people with better understanding of the system? Also worth noting that like in any profession the most writers are... mediocre. Especially when you hire someone on contract. I had mostly bad experience with them in past. They happily charge $1000 for a few pages of garbage that is not even LLM-quality. No creativity, just pumping out words.

I can chip in like $20 to pay some "good writer" that "observes, listens and understands" for writing documentation on something and compare it with LLM-made one.

"Write a manual for air travel for someone who never flew. Cover topics like buying a ticket, preparing for travel, getting to airport, doing things in the airport, etc"

Let's compare!

Many engineers are terrible at documentation, not just because they find it boring or cannot put it into words (that's the part an LLM could actually help with) but because they cannot tell what to document, what is unneeded detail, how best to address the target audience (or what is the profile of the target audience to begin with; something you can tell an LLM but which it cannot find on its own), etc, etc. The Fine Article goes into these nuances; it's the whole point of it.

> "Write a manual for air travel for someone who never flew. Cover topics like buying a ticket, preparing for travel, getting to airport, doing things in the airport, etc"

Air travel is a well-known thing, surely different from your bespoke product.