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.
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.
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.
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).
See Duolingo :)
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.
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.
- 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.
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.
Nonetheless, I live from that work. If you are correct, there's a fair bit of money on the table for you.
I could say I make my living with various forms of communication.
I am not worried about AI replacing me 1:1 communication wise. What is going to happen is the structure around me that gives my current communication skill set value is going to change so much and be optimized to a degree that my current skill set is no longer going to have much value in the new structures that arise. I will have to figure out how to fit into these new structures and it surely won't be doing the same thing I am doing now. That seems so obvious.
As as writer, you know this makes it seem emotional rather than factual?
Anyway, I agree with what you are saying. I run a scientific blog that gets 250k-1M users per year, and AI has been terrible for article writing. I use AI for ideas on brainstorming and ideas for titles(which ends up being inspiration rather than copypaste).
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.
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
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.
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.
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.
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.
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.
Thank you for putting this so eloquently into words. At my work (FAANG) tech writers are being let go and their responsibilities are being pushed on developers, who are now supposed to “use AI” to maintain customer facing documentation.
Is this the promise land? It sure doesn’t feel like it.
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.
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.
- 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.
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.
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.
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.
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.
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.
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.
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.
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?
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.
Kudos to all technical writer who made my job as software engineer easier.
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.
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?
We fired our professional tech writers. They've been using AI all the time (with horrible results), and were basically incapable of tech writing without it at all.
Looking for tech writers on the market is nigh impossible. Even people with decent portfolio tend to be very bad at their job.
The only good option now is to hire a software developer to do the writing. There's a decent amount of them who have experience with that. Obviously devs won't like to have it on their CV instead of proper development.
Honestly this is a catastrophe. If you're firing a tech writer that writes something even semi-decent, you're ruining your business.
Reminder: AI is only good at things that existed in bulk during its training, such as README files, configs that always look the same (package.json, dockerfile), and tests. The documentation for your product, or for products of that kind, or even in general, either never existed, or not such a commodity to have AI generate it well.
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
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.
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.
But most people aren't that great at their jobs.
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.
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)
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.
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.
Good human written docs > AI written docs > no docs > bad human written docs
I think this is going to be a defining theme this year.
The parts about hallucinations and processes are also a bit dated. We're either at, or very close to the point where "agentic" stuff works in a "GAN" kind of way to "produce docs" -> read docs and try to reproduce -> resolve conflicts -> loop back, that will "solve" both hallucinations and processes, at least at the quality of human-written docs. My bet is actually better in some places. Bitter lesson and all that. (at least for 80% of projects, where current human written docs are horrendous. ymmv. artisan projects not included)
What I do agree with is that you'll still want someone to hold accountable. But that's just normal business. This has been the case for integrators / 3rd party providers since forever. Every project requiring 3rd party people still had internal folks that were held accountable when things didn't work out. But, you probably won't need 10 people writing docs. You can hold accountable the few that remain.
There are plenty of people who can read code who don't work as devs. You could ask the same about testers, ops, sysadmins, technical support, some of the more technical product managers etc. These roles all have value, and there are people who enjoy them.
Worth noting that the blog post isn't just about documenting code. There's a LOT more to tech writing than just that niche. I still remember the guy whose job was writing user manuals for large ship controls, as a particularly interesting example of where the profession can take you.
Also, the primary task of a tech writer isn't to document code. They're supposed to write tutorials, user guides, how to guides, explanations, manuals, books, etc.
The other way around. For example the Python C documentation is full of errors and omissions where engineers described what they thought should happen. There is a documentation project that describes what actually happens (look in the index for "Documentation Lacunae"): https://pythonextensionpatterns.readthedocs.io/en/latest/ind...
My advise to tech writers would be to get really good at directing and orchestrating AI tools to do the heavy lifting of producing documentation. If you are stuck using content management systems or word processors, consider adopting a more code centric workflow. The AI tools can work with those a lot better. And you can't afford to be doing things manually that an AI does faster and better. Your value is making sure the right documentation gets written and produced correctly; correcting things that need correcting/perfecting. It's not in doing everything manually; you need to cherry pick where your skills still add value.
Another bit of insight is that a lot of technical documentation now has AIs as the main consumer. A friend of mine who runs a small SAAS service has been complaining that nobody actually reads his documentation (which is pretty decent) and instead relies on LLMs to do that for them. The more documentation you have, the less people will read all of it. Or any of it.
But you still need documentation. It's easier than ever to produce it. The quality standards for that documentation are high and increasing. There are very few excuses for not having great documentation.