AI

The author argues that relying on AI to produce substantive writing undermines the depth, authenticity, and critical thinking essential to meaningful creative work. They highlight that AI-generated content often lacks nuance and originality, which are crucial for developing a distinctive voice and engaging storytelling. Instead, the piece advocates for writing processes that prioritize human insight and intentionality over automated shortcuts.

https://erichgrunewald.substack.com/p/why-you-should-almost-never-use-ai

The article advises writers to use large language models (LLMs) as copyediting tools rather than ghostwriters, emphasizing two key rules: never use LLM-suggested words verbatim and avoid taking the model’s encouragement at face value to preserve authentic voice. LLMs excel at spotting technical issues like overused phrases or passive voice, so writers should draft independently, then employ models to identify problems and assist with iterative editing while maintaining control over content and style.

https://sockpuppet.org/blog/2026/09/17/how-to-write-with-an-llm/

The article explores common patterns and tendencies in AI-generated writing, highlighting its frequent reliance on formulaic structures and predictable phrasing. It suggests approaches for writers to recognize these AI habits and employ strategies that enhance originality and depth, thereby improving the creative and editorial process when integrating AI tools.

https://a16zcrypto.substack.com/p/the-habits-of-ai-writing-and-what

The article argues that writing is likely the safest job from AI disruption because large language models (LLMs) consistently produce formulaic, soulless prose and struggle to achieve authentic expression or insight. Writing is characterized as a “wicked problem” with no clear specifications or objective verification, requiring continuous mental modeling of the reader—capabilities that current AI lacks. Consequently, human writers retain a comparative advantage by providing authentic, creative communication that AI cannot replicate, making genuine human writing a valuable and costly signal in the AI era.

https://muratbuffalo.blogspot.com/2026/08/the-safest-job-from-ai-may-be-writing.html

James Zhan critiques efforts to distinguish AI-generated writing from human writing, arguing that commonly cited “tells” are misleading and oversimplified, especially when based on limited examples like a New York Times literary experiment. He emphasizes that good writing—whether human or AI—varies by genre and purpose, and clear, accessible writing should not be mistaken for AI output. Zhan warns that assumptions about writing origin unfairly penalize skilled writers and that AI is fully capable of producing diverse styles, challenging the notion that one can reliably detect AI-generated text.

https://journal.james-zhan.com/ai-writing/

Sophie Alpert argues that AI-generated text currently cannot reliably preserve an author’s intended meaning without careful human review and editing. She emphasizes that writing is an essential thinking process, and authors must fully own and understand every part of their writing to communicate clearly and respect readers' time. While AI can assist in brainstorming or proofreading, authors should avoid relying on it for unedited content and instead use it as a tool to support, not replace, their own detailed thought and expression.

https://sophiebits.com/2026/06/25/there-are-no-lossless-transformations-of-natural-language-text