Documentation Isn’t Dying, But Bad Production Habits Should Be Very Nervous
AI may speed drafting, but it cannot replace structured, governed content people trust and machines can interpret
Every few years, someone declares technical documentation dead with the self-assurance of a keynote speaker who has never had to explain a failed integration to an angry customer. Documentation, meanwhile, keeps stubbornly refusing to die.
👉🏾 Users still need it.
👉🏾 Support teams still depend on it. A
👉🏾 AI systems are increasingly consuming it as source material
The need hasn’t disappeared. What has changed is the environment in which documentation gets created, maintained, delivered, and now interpreted by machines. That’s the central point Ondrej Tesar (documentation lead at Pricefx) makes in a recent LinkedIn post, and it’s a useful one.
Tesar argues that AI-assisted coding is accelerating software development while doc teams risk falling behind. He points to a structural asymmetry: software teams often benefit from cleaner, more machine-friendly inputs, while documentation work still depends heavily on context gathering, judgment, and knowledge that may not be fully captured in formal systems. He also argues that the hardest part of the work lives in the undocumented remainder: tribal knowledge, edge cases, and design rationale that do not show up neatly in publicly available product information.
That argument is directionally right, but it needs one important correction.
Not All Doc Teams Are Working From A Swamp Of Vague Jira Tickets
Not all documentation teams are working from a swamp of vague Jira tickets, random Slack messages, and whatever an engineer muttered before running to another meeting. Some teams have spent years building semantically structured, metadata-rich content operations using XML, DITA, controlled vocabularies, reuse models, governed workflows, and component content management systems.
Those teams aren’t merely “writing docs.” They’re building machine-processable knowledge assets. And in a world increasingly shaped by retrieval, summarization, answer engines, agents, and large language models, that matters a great deal.
This distinction isn’t trivial. It is the whole ballgame.
The Capability Multiplier: Semantic Structured Content
When docs are semantically structured, they become easier to retrieve, reuse, validate, govern, securely personalize, and transform. It becomes easier for both humans (and those pesky machines upon which we rely) to determine what a chunk of content is, how it relates to other chunks, when it applies, what conditions constrain it, and whether it belongs in a given output.
Structured content does not magically solve every documentation problem, but it gives us a far stronger foundation for solving them. That’s been true for years. AI just made the consequences easier for us to see.
