The Cognitive Cost of AI-Generated Writing

Writing is a vital process for developing understanding and establishing professional credibility. Outsourcing this task to LLMs prevents personal growth and signals a lack of authentic engagement with the subject matter. To maintain trust and capability, writers should use AI for research and brainstorming while keeping the actual writing process human.

Key Points

Writing is a cognitive tool used to transform murky ideas into structured understanding.
Using AI to write is a missed opportunity for personal growth and mental strengthening.
AI-generated documents undermine professional credibility and the perceived authenticity of the author's ideas.
LLMs are better suited for research, transcription, and generating raw ideas rather than final drafts.
As AI increases technical efficiency, there must be a corresponding rise in human thoughtfulness.

Sentiment

The community strongly agrees with the article's core thesis. Most commenters affirm that writing is fundamentally a thinking process and that outsourcing it to AI is cognitively costly. However, the agreement is not absolute — there is a well-represented pragmatic middle ground acknowledging legitimate uses for boilerplate, non-native language assistance, and iterative collaboration. The most heated exchanges occur between AI skeptics who see any LLM use as intellectual laziness and pragmatists who argue the distinction lies in whether writing serves thinking or mere ritual.

In Agreement

Writing is the 'last step in thinking' — ideas that seem clear in your head often fall apart when you try to commit them to text, revealing hidden contradictions that need resolving
AI-generated writing shifts the burden from writer to reader, creating a 'workslop' problem where the real work becomes reviewing and correcting AI output rather than producing thoughtful content
Running text through an LLM strips it of personality, opinion, and the subtle signals that build trust with readers — it reduces the information content of writing
Cognitive deskilling is a serious concern, especially for students who may never develop independent thinking abilities if they rely on LLMs before learning to write on their own
The same principle applies to code — writing code is also thinking, and if LLMs had existed earlier, innovations like Rails and Django might never have emerged from the pain of repetitive work
Nobody actually wants to consume LLM output — lots of people want LLMs to produce things, but nobody wants to read what comes out, creating a fundamental market dysfunction

Opposed

Not all writing is thinking — much workplace writing is 'ritualized context transfer' (ceremonies, boilerplate docs, release notes) where AI is entirely appropriate and saves pointless toil
For non-native English speakers, LLMs are invaluable for expressing ideas clearly, and being understood is more important than struggling through the writing process
AI can serve as a productive collaborative partner when used iteratively — providing structural feedback, acting as an editor, and helping overcome writer's block while the human retains ownership of ideas
Writing is not the only way to facilitate thinking — dictation, audio recording, and other modalities can serve the same cognitive purpose, and AI can help with transcription and cleanup
AI conversation can actually create MORE friction and writing, not less — one commenter reports typing 10 words in chat for every 1 word of final LLM output, with monthly word count increasing 5-10x