Every conversation about AI for technical writers eventually lands in the same place.
Someone asks: Can AI do this job?
It’s the wrong question. And the fact that most of us are still fixated on it is actually slowing us down more than AI ever could.
Here’s what’s happening in the real world, as opposed to the discourse world. Technical writers are using AI every day — drafting release notes, summarizing specs, generating first-pass outlines, and checking terminology consistency. It’s not revolutionary. It’s Tuesday. A McKinsey survey found that by late 2025, 88% of companies reported using AI regularly across at least one business function. For documentation teams, that shift from experiment to everyday tool happened quietly — and for most of them, the novelty wore off months ago. The market now offers numerous options of AI for technical writers, and many are even writing-specific. I wrote a guide about 50 AI writing tools here.
Meanwhile, the conversations online are still stuck in 2023: Will AI replace technical writers? Should we be scared? Is this the end?
No. No. And no. But here’s the thing — the reason it’s not the end isn’t what most people think. Understanding the actual reason matters because it points directly to where the real value in this profession is shifting.
The Assumption That’s Causing All the Anxiety
The “AI will replace us” fear rests on a simple assumption: that technical writing is fundamentally a text-generation task. You take information in, you produce clear documentation out. If AI can generate text, it can do the job.
That assumption is wrong. Not slightly wrong — structurally wrong. And once you see why, the anxiety starts to dissolve, and something more useful takes its place.
Technical writing has never really been about generating text. It’s been about judgment. Judgment about what the reader needs to know, in what order, at what level of detail. Judgment about what’s accurate and what’s not. Judgment about what will confuse someone at 11 PM when they’re trying to get an integration working and your documentation is the only thing standing between them and a functioning product.
AI is excellent at generating text. It is genuinely bad at judgment. And in technical writing, judgment is everything.
The Question That Actually Matters
So if the wrong question is “Can AI do this?” — what’s the right one?
It’s this: Can you tell when AI is wrong?
That might sound like a small shift. It isn’t. It changes your entire relationship to the tool, to your role, and to the work itself.
Here’s a concrete example. Skyflow built a custom AI workflow, VerbaGPT, to manage their technical documentation as they scaled across AWS regions. Their case study, published on the AWS blog, reported that it cut their overall documentation cycle from roughly three weeks down to five days. Genuinely impressive. But the workflow didn’t end at the draft. It ended after the technical writing team reviewed every piece of that output for accuracy, compliance, and clarity before anything shipped.
Those writers weren’t doing less work. They were doing different work. And in many ways, harder work. Because reviewing AI output critically is not the same thing as writing from scratch. It requires you to know the material well enough to catch what sounds right but isn’t. AI doesn’t hedge. It doesn’t say “I’m not sure about this.” It produces confident prose, whether it’s accurate or not.
That’s not a flaw to work around. It’s the defining challenge of technical writing in 2026. It’s a challenge that requires exactly the kind of deep, contextual expertise that no language model currently offers.
What “Telling When AI Is Wrong” Actually Looks Like in Practice
This isn’t abstract. It shows up in specific, recurring ways, and knowing them makes you better at the job immediately.
Hallucinated Specifics
AI will invent API parameters, version numbers, and configuration options that don’t exist. If you don’t know the product well enough to notice, those fabrications ship as documentation. In technical writing, that’s not just embarrassing — in regulated industries, it can be genuinely dangerous. The EU AI Act, which becomes fully enforceable for high-risk AI systems in August 2026, requires detailed technical documentation to demonstrate that a system meets regulatory requirements before it ships. Fines run up to 7% of global annual turnover. A fabricated specification in that documentation isn’t just an accuracy problem. It’s a legal one.
Tone Drift
AI has a default register. It’s confident, slightly generic, and tends toward the corporate. Your documentation might need to be conversational for developers, precise for compliance teams, or empathetic for end-users who are struggling. AI will miss that unless you’re specific about it — and even then, you need to verify.
Missing Context
AI doesn’t know that your users tried three other solutions before landing on yours. It doesn’t know that a particular feature was confusing in the last release and that your docs need to address it proactively. In fact, it can’t know these things. You can.
Structural blind spots
AI can produce a well-organized document that buries the most important information in paragraph four. It doesn’t understand what your reader is trying to accomplish. It doesn’t know that someone reading your troubleshooting guide is already frustrated and needs the answer in the first two sentences.
Catching these things isn’t proofreading. It’s a skill—one that combines deep product knowledge, audience understanding, and a specific kind of critical reading that sharpens with practice.
Why This Is Actually Good News
I want to be honest: the technical writing job market right now is tighter than it has been. Across the community, experienced writers are reporting that hiring for generalist roles has flattened and that some teams aren’t backfilling when writers leave. The pressure to produce more with fewer people is real.
But here’s what’s also real: the writers who are being hired, retained, and promoted are the ones doing exactly what I’m describing. They’re not the ones who use AI the most. They’re the ones who use it best — which means knowing where it adds value and where it falls short, and building workflows that leverage the former without being exposed to the latter.
The World Bank’s Digital Progress and Trends Report 2025 found that job postings requiring generative AI skills surged ninefold between 2021 and 2024. That’s not a coincidence. The market is actively seeking people who can work effectively with these tools. Not people who can prompt the fastest. People who understand the output deeply enough to ship documentation that’s actually trustworthy.
That’s a higher bar than what technical writing used to be. But it’s also a more interesting job. And it’s one that AI genuinely cannot do on its own.
The Practical Shift — AI For Technical Writers
If you’re a technical writer reading this and thinking about what to actually do with it, here’s where I’d start — not with a tool, but with a habit.
The next time you use AI to help with a piece of documentation, don’t just read the output for flow and grammar. Read it like someone who knows the product inside and out, and is actively looking for the seams — the places where the AI is filling in gaps with plausible-sounding but unverified information. Notice where it’s vague. Notice where it sounds confident, but you can’t immediately verify the claim. Notice where it’s missed something that any real user would need to know.
That’s the muscle. And it’s the one that matters most right now.
It’s also, for what it’s worth, the muscle that makes you better at the job regardless of whether AI is in the picture. Critical, contextual reading has always been the foundation of good technical writing. AI just makes it more visible — and more necessary — than it’s ever been.
There’s a second shift worth paying attention to — one that the “AI will replace us” conversation tends to miss entirely. Your documentation isn’t just being written with AI anymore. It’s being read by AI. Throughout 2025, a standard called llms.txt began gaining traction—a structured file that tells AI systems like ChatGPT and Perplexity where a product’s authoritative documentation lives. By late 2025, over 844,000 websites had adopted it, including Anthropic, Cloudflare, and Stripe. The implication is straightforward: how you structure your documentation now directly affects how accurately AI represents your product when someone asks about it. That’s not a future concern. It’s already happening.
The Bigger Picture
We’re at an odd moment in this profession. The tools are genuinely powerful. The anxiety is genuinely understandable. The actual path forward is less dramatic than either the hype or the fear suggests.
AI didn’t replace technical writers. It shifted the job’s focus. And what it’s really about — understanding your audience, knowing your product, exercising judgment about what’s true and what’s useful — is the part that was always the most important anyway.
Some in the profession are already giving this a name. In a September 2025 conversation between Tom Johnson and Fabrizio Ferri Benedetti — two of the more thoughtful voices in tech comm — they described the emerging role as “context curator”: someone who doesn’t just write documentation, but architects the knowledge systems that both humans and AI agents draw from. It’s a useful frame for what’s actually happening.
The writers who figure that out first aren’t just surviving this transition. They’re building the kind of expertise that will be harder, not easier, to replicate as tools become more capable.
That’s where I’d put my energy. Not on whether AI can do this job. On whether you can do it better than AI can — and on becoming someone who clearly, undeniably can.
References
European Parliament and Council of the European Union. “Regulation (EU) 2024/1689 — Artificial Intelligence Act.” Official Journal of the European Union, 13 June 2024. https://artificialintelligenceact.eu/
Ferri Benedetti, Fabrizio. “AI must RTFM: Why technical writers are becoming context curators.” Passo.uno, 8 August 2025. https://passo.uno/from-tech-writers-to-ai-context-curators/
Getpublii. “The Complete Guide to llms.txt: Should You Care About This AI Standard?” 23 October 2025. https://getpublii.com/blog/llms-txt-complete-guide.html (Source for llms.txt adoption figures via BuiltWith tracking.)
Howard, Jeremy. “llms.txt: A standard for giving LLMs context about a website.” Answer.AI, September 2024.
Johnson, Tom, and Fabrizio Ferri Benedetti. “How AI is changing the role of technical writers to context curators and content directors.” Podcast. I’d Rather Be Writing, 28 September 2025. https://idratherbewriting.com/blog/podcast-fabri-tom-sept-episode-1
McKinsey & Company. “The state of AI in 2024 and beyond: Moving from experimentation to deployment.” McKinsey Digital, 2024.
Skyflow. “How Skyflow Creates Technical Content in Days Using Amazon Bedrock.” AWS Machine Learning Blog, 2024. https://aws.amazon.com/blogs/machine-learning/how-skyflow-creates-technical-content-in-days-using-amazon-bedrock/
World Bank Group. Digital Progress and Trends Report 2025. World Bank, 2025.
