Sharing raw AI-generated text without review damages professional relationships and reduces comprehension. Large language models produce content quickly but create an effort imbalance where readers spend the same time parsing text that took seconds to generate. This asymmetry compounds when verbose AI responses flood conversations, effectively filibustering discussions.

Before AI tools, writing required deliberate thought and effort from authors, creating a natural balance with readers' investment. Studies show delegating thinking to AI creates cognitive debt, reducing actual understanding of topics. Modern LLMs also generate text with authoritative confidence that masks uncertainty, making it impossible for recipients to gauge the sender's true expertise.

Trust suffers when AI output enters professional communication. Recipients cannot verify what the sender actually checked or understand the chain of responsibility for errors. This creates uncertainty about whether mistakes stem from AI hallucination or human oversight. The result is a breakdown in professional trust where recipients must treat all AI-shared content as potentially unreliable, regardless of the sender's intentions.