Content Loses Visibility in AI Answers

Industry analysts warn that high-quality web content often fails to appear in LLM-based answers because models prioritize machine-useful passages over human relevance. Research from 2025 and guidance from NIST and BrightEdge show retrieval systems can omit, misroute, or be distracted by content, reducing answer accuracy and discovery. Practitioners are advised to measure a 'Utility Gap' by testing intent-driven prompts across AI platforms and tracking citations, brand mentions, and routing.