Erich Grunewald’s latest essay, now circulating on Hacker News, delivers a blunt warning to developers and writers alike: AI tools should almost never be used to generate substantive content. The piece argues that automated drafting is eroding the quality of human communication in technical fields by removing the essential friction required for deep thought.

The Illusion of Productivity

The piece challenges the prevailing narrative that AI writing assistants are productivity multipliers. For builders and technical writers, the temptation to offload documentation or blog posts to an LLM is high. However, the author argues that this convenience comes at the cost of clarity and authenticity. Substantive writing requires a distinct voice and logical flow that current models struggle to replicate without introducing generic filler.

Defining 'Substantive' Work

Grunewald draws a sharp line between low-stakes and high-stakes writing. 'Substantive' content is defined as material that conveys complex ideas, nuanced arguments, or precise technical instructions. This includes architecture decision records, detailed bug reports, and thought-leadership essays. In these contexts, the specific intent and deep understanding of the human author are paramount, not just grammatical correctness.

The Mechanism of Failure

AI models, by design, predict the next likely token based on statistical probabilities. When applied to substantive tasks, this results in text that sounds professional but lacks specific insight. The essay suggests that the output often feels 'smoothed over,' lacking the jagged edges of genuine human experience or the precise logical jumps that characterize expert technical writing.

The Hidden Cost of Editing

Contrary to the promise of speed, the author notes that using AI for substantive drafting often leads to a net loss in time. Fact-checking AI-generated technical assertions, correcting subtle logical errors, and rewriting passages to restore a human voice takes significantly longer than writing the initial draft from scratch. The cleanup phase becomes a burden that negates the initial drafting speed.

Homogenization of Technical Discourse

Over-reliance on LLMs for drafting risks creating a homogenized technical landscape. When everyone uses the same models to write documentation and blogs, the distinct voices of different engineering cultures fade. The essay warns that we are drifting toward a world where all technical communication sounds like a generic corporate memo, stripping away the personality that helps readers connect with the author’s expertise.

The Proposed Alternative Workflow

Grunewald does not banish AI entirely but proposes a strict workflow: use AI for brainstorming, outlining, or summarizing existing notes, but never for final drafting. The human must remain the primary generator of ideas and prose. AI can serve as a critic or a syntax checker, but it should not be the author. This preserves the cognitive engagement necessary for producing high-quality, substantive work.

Key Takeaways

  • AI is best suited for brainstorming or rough outlines, not final drafts of important documents.
  • Human oversight is mandatory for substantive technical writing to ensure accuracy and tone.
  • Over-reliance on LLMs for drafting can lead to a homogenization of technical discourse.
  • The time saved in drafting is often lost in the heavy editing and fact-checking required for AI text.

The Bottom Line

Stop letting LLMs ghostwrite your critical technical documentation; use them for structure, but keep the actual prose in human hands to maintain credibility and clarity.