Do your own writing
Large language models are changing how people write, but many argue that outsourcing prose — and especially ideas — to AI undermines both genuine thinking and trust in what’s written. Commenters distinguish between using AI as a tool (for outlining, editing, summarizing, or handling low‑stakes “ceremony” documents) and letting it generate core arguments, fiction, or student work, which they see as hollowing out skill, creativity, and responsibility. There is broad agreement that writing is a crucial part of how humans reason and learn, and concern that pervasive AI‑generated text shifts effort and cognitive work from authors onto readers while encouraging conformity and “slop” in public content.
Role of Writing in Thinking
- Many see writing (and coding) as a core thinking tool: ideas that feel clear in your head often fall apart when written, revealing gaps and contradictions.
- Letting LLMs produce the main text is likened to “paying someone to work out for you”: you lose the cognitive gains from the effort.
- Several argue that writing for yourself can be messy and private; the “polished essay” is a different activity from thinking-through-writing.
When AI Helps vs. Hurts
- Common “good uses”: grammar/style cleanup, summarization, outlining, transcript cleanup, generating quizzes/worksheets, and identifying weak spots in existing text or lyrics.
- LLMs are valued as conversational partners / rubber ducks by some, helping organize thoughts, expose edge cases, and provide feedback.
- Others argue this is not true “rubber ducking” and risks offloading too much comprehension and judgment.
Authenticity, Trust, and Workplace Dynamics
- Once readers suspect LLM authorship, they feel they’re reviewing the model’s work, not the author’s thinking.
- In teams, workers who have AI write design docs, PRs, or slides and then rely on colleagues for review are seen as offloading both creation and self-review.
- Some suggest for purely ceremonial or unread documents, AI authorship is acceptable and efficient.
Idea Generation and “Average” Output
- Disagreement on whether LLMs are good at generating ideas:
- Critics say output is bland, mainstream, and unoriginal.
- Supporters see value in enumerating options, surfacing missed pros/cons, or nudging them out of blocks—even if many suggestions are discarded.
- There’s concern that relying on AI for ideation may constrain thought to what the model finds salient.
Skills, Education, and Equity
- Strong worry that students using LLMs for core writing will stunt their ability to think and write independently.
- Others note LLMs are powerful assistive tools for non-native speakers, ADHD, and people who struggle with formal prose, enabling clearer communication.
- Several emphasize that the key is conscious use: don’t let AI replace the personally valuable parts of thinking and learning.