AI agents can emulate the beliefs of human survey respondents. We leverage this idea at scale to examine how investor disagreement emerges in response to firm news and to measure such disagreement at high frequency. We endow a local large language model (Llama 3) with over 200 demographically representative investor personas and elicit their sentiment toward S&P 500 firm-specific news headlines from 2010–2025. LLM personas disagree in economically meaningful ways: disagreement is largest for social and governance-related news, and smallest for hard news tied to firm fundamentals. The measure aligns with human survey evidence and traditional uncertainty measures, yet it is distinct from social-media disagreement. Turning to market outcomes, LLM-derived disagreement is strongly associated with same-day and next-day abnormal trading volume. Our main findings are stable across the model’s pre- and post-training windows.