A critical examination of the trend to replace technical writers with AI-generated documentation, exploring the hidden costs, liability risks, and the fundamental misunderstanding of what technical documentation actually accomplishes. The piece argues that AI should augment, not replace, the human expertise that makes documentation effective.
The decision to eliminate technical writing roles in favor of AI-generated documentation represents a fundamental misunderstanding of both the technology's capabilities and the actual function of documentation. When companies fire writers or refuse to hire them, believing LLMs can handle the task, they're not just cutting costs—they're introducing new risks while failing to understand what makes documentation work.
The Illusion of Output vs. Process
When executives look at AI-generated documentation, they see clean, plausible prose. What they miss is the invisible process that makes documentation valuable. Technical writers don't just write words; they conduct interviews with engineers, navigate organizational politics to get accurate information, understand user psychology, and make judgment calls about what to document and what to omit.
An LLM can generate text that looks like documentation, but it cannot conduct a stakeholder meeting to resolve conflicting information about a feature's behavior. It cannot read between the lines when a developer says "that should work" but means "I haven't tested it thoroughly." It cannot understand the political implications of documenting a feature that's being sunsetted or the strategic value of highlighting a new capability.
The writer's role extends far beyond transcription. They are product truth-tellers who must balance accuracy with accessibility, completeness with clarity. They make judgment calls about edge cases that matter to users but might seem trivial to engineers. This requires empathy—the ability to understand what users don't know and what they need to know, which is fundamentally different from pattern matching.
The Liability Problem No One Wants to Discuss
When documentation causes harm, liability doesn't evaporate because an AI wrote it. Consider a scenario where an AI-generated runbook instructs a system administrator to run a command that, due to a subtle edge case, corrupts a database. The documentation appears correct—it uses proper terminology and follows a logical sequence—but fails to account for a specific environment configuration.
Who is responsible? You cannot depose an LLM. You cannot fire a model. The legal system requires a human or corporate entity to hold accountable. The company that published the documentation remains liable, regardless of who or what wrote it. This creates a dangerous gap: the organization bears all the risk while losing the human judgment that might have caught the error.
Insurance companies are beginning to recognize this. Some cyber insurance policies now question whether AI-generated security documentation meets "reasonable care" standards. The legal concept of "duty of care" implies human judgment and expertise. An LLM cannot exercise judgment—it can only generate text based on patterns in its training data.
The Context Problem: Why AI Needs Technical Writers More Than They Need AI
Paradoxically, the companies that fire technical writers often find their AI tools performing worse, not better. This isn't surprising when you understand how modern AI systems work. Tools like Claude Skills, Cursor rules, and RAG (Retrieval-Augmented Generation) systems depend entirely on the quality of their context.
Context curation is technical writing under a different name. It requires understanding what information matters, how to structure it for retrieval, and how to label it for relevance. When you eliminate the people who create high-quality context, you're removing the supply chain for the intelligence your AI tools depend on.
Consider a company that fires its technical writers and then wonders why their internal AI assistant gives inconsistent answers about their own products. The assistant fails because no one is maintaining the curated knowledge base that makes accurate answers possible. The writers you let go were the architects of that knowledge infrastructure.
The Augmentation Path Forward
The solution isn't to reject AI entirely, but to understand its proper role. Technical writers augmented with AI tools can achieve remarkable productivity gains while maintaining quality. This isn't theoretical—it's already happening.
An augmented technical writer might use AI to:
- Generate first drafts of routine documentation, freeing time for complex analysis
- Identify inconsistencies across documentation sets
- Suggest alternative explanations for difficult concepts
- Automate formatting and style consistency checks
But the human writer remains in control, making judgment calls about accuracy, relevance, and user experience. They understand when an AI suggestion is wrong, when it misses important context, or when it oversimplifies a complex topic.
This model requires investment in training and tools, not elimination of roles. It requires developing an AI policy for documentation that defines when AI can be used, when human review is mandatory, and how to handle edge cases. Most importantly, it requires giving technical writers the time and resources to experiment with AI tools and develop workflows that work for their specific context.
The Human Element That Can't Be Automated
Great documentation requires understanding what users don't know. This is fundamentally different from knowing what users might ask. Technical writers develop this understanding through direct interaction with users, analysis of support tickets, and observation of how people actually use products.
An LLM cannot sit in on a user research session and notice the moment when a participant's confusion turns to frustration. It cannot read the subtext in a support ticket where a user describes a problem but doesn't understand what's causing it. It cannot develop the intuition that comes from years of seeing patterns in how people struggle with technology.
This empathy gap is why AI-generated documentation often feels hollow. It contains information but lacks insight. It answers questions but doesn't anticipate needs. It provides instructions but doesn't build confidence. These are the qualities that separate functional documentation from great documentation.
The Strategic Cost of Short-Term Thinking
Companies that eliminate technical writing roles often see short-term cost savings. They may also see short-term productivity gains as engineers pick up documentation tasks. But these gains are illusory.
Engineers who spend time writing documentation are engineers who aren't building features. The quality of their documentation is typically lower because they lack writing expertise and user empathy. More importantly, they lack the time to develop the deep understanding of user needs that makes documentation effective.
The long-term costs compound. Poor documentation increases support costs, slows user adoption, and damages brand reputation. It creates technical debt that becomes harder to address over time. It forces engineers to repeatedly answer the same questions because the documentation doesn't provide clear answers.
A Call for Reconsideration
For companies that have eliminated technical writing roles or chosen not to hire them, the path forward requires reconsideration. This doesn't mean abandoning AI tools, but rather integrating them thoughtfully into a human-centered documentation strategy.
Start by recognizing that documentation is a product feature, not a cost center. It directly impacts user success, support costs, and product adoption. Invest in the people who understand how to make that feature work.
Then, provide those people with AI tools and training. Give them time to experiment and develop workflows. Create policies that ensure quality while enabling innovation. Most importantly, trust their expertise about what works and what doesn't.
The companies that will succeed in the AI era aren't those that replace humans with machines, but those that augment human expertise with machine capabilities. Technical writers are uniquely positioned to lead this augmentation because they already understand how to translate complex information into accessible formats.
The question isn't whether AI will change technical writing—it already has. The question is whether companies will approach this change strategically, recognizing that the goal is better documentation, not cheaper documentation. And better documentation requires human judgment, empathy, and expertise that no AI can replicate.
The writers you let go understand this. They understand the difference between noise and signal, between information and insight, between words that look right and words that work. Bring them back, give them the tools they need, and watch what happens when technology serves human expertise rather than trying to replace it.
For those interested in exploring this topic further, the technical writing community has been actively discussing these issues. Resources like Write the Docs and Tom Johnson's I'd Rather Be Writing blog provide ongoing insights into how technical writers are adapting to AI while maintaining quality standards.
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