I write documentation for a living. Although my output is writing, my job is observing, listening and understanding. I can only write well because I have an intimate understanding of my readers' problems, anxieties and confusion. This decides what I write about, and how to write about it. This sort of curation can only come from a thinking, feeling human being.

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.

A somewhat related anecdote:

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"

The best tech writers I have worked with don’t merely document the product. They act as stand-ins for actual users and will flag all sorts of usability problems. They are invaluable. The best also know how to start with almost no engineering docs and to extract what they need from 1-1 sit down interviews with engineering SMEs. I don’t see AI doing either of those things well.

However, the writing is on the wall: AI will completely replace technical writers.

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.

Yeah. AI might replace tech writers (just like it might replace anyone), but it won't be a GOOD replacement. The companies with the best docs will absolutely still have tech writers, just with some AI assistance.

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.

The best tech writers I've known have been more like anthropologists, bridging communication between product management, engineers, and users. With this perspective they often give feedback that makes the product better.

I'm currently in the middle of restructuring our website. 95% of the work is being done by codex. That includes content writing, design work, implementation work, etc. But it's a lot of work for me because I am critical about things like wording/phrasing and not hallucinating things we don't actually do. That's actually a lot of work. But it's editorial work and not writing work or programming work. But it's doing a pretty great job. Having a static website with a site generator means I can do lots of changes quickly via agentic coding.

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.

The failure mode isn't just hallucinations, it's the absence of judgment: what not to document, what to warn about, what's still unstable, what users will actually misunderstand

And here I am, 2026, and one of my purposes for this year is to learn to write better, communicate more fluently, and convey my ideas in a more attractive way.

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.

>> liability doesn’t vanish just because AI wrote it

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