AI as an Informational Intermediary

Large language models (LLMs) are becoming a primary interface for investor opinion formation, shifting belief formation upstream into private AI conversations. We document a systematic bias in AI-assisted forecasting: sycophancy, the tendency to validate a user-stated position beyond what the available evidence justifies. We develop a model in which sycophancy arises when the system treats the user's stated leaning as informative and, at the same time, faces incentives to communicate in a simple and decisive way. The first force tilts beliefs toward agreement; the second converts moderate uncertainty into categorical recommendations. Embedding this mechanism in a sequential learning environment implies faster cascade formation and higher error risk because publicly shared AI messages inject directional bias and false confidence into the information history. Using "Due Diligence" posts from Reddit's r/wallstreetbets, we generate controlled AI replies under neutral, retail, and professional personas. Default personas exhibit excess agreement of 0.4-0.5 sentiment points relative to human replies, while a professional prompt eliminates the effect. Entity-neutering leaves agreement unchanged, ruling out lookahead explanations.