Do AI Assistants Reinforce Investor Beliefs? Evidence from Human and AI Replies

Using Reddit r/wallstreetbets posts, we compare the sentiment of AI-generated replies with actual human top-level replies to the same investor-authored posts. We find that counterfactual replies generated by a user-facing AI model are systematically more bullish than human replies. Prompting the model to respond as a professional investor narrows the gap, but does not eliminate it. To study potential mechanisms, we compare the user-facing model with a matched base model that shares the same architecture and pretraining corpus but lacks instruction-following post-training and chat-format deployment. The instruction-tuned model is consistently more bullish than the matched base model, suggesting that the post-trained conversational layer amplifies bullishness beyond what is already present in the base model. We rationalize the evidence with a reduced-form model in which replies reflect a learned bullish default, responsiveness to the user’s thesis, and categorical expression. Finally, we examine whether AI-augmented signals retain return-relevant content. The return evidence is descriptive: AI-augmented signals remain positively associated with near-horizon returns in the main specifications, but the relation is not uniform across samples or horizons.