This study exploits the non-fungible token (NFT) setting to estimate the value of non-pecuniary benefits, a long- standing empirical challenge in private-value markets such as art, antiques, and other collectibles. After developing and validating our emotional dividend proxy (LOVE), we apply deep learning algorithms and discover that contemporaneous price fluctuations, certain collection features, and ownership wealth significantly contribute to its formation. Leveraging these parameters, we employ AI-powered models to estimate NFT prices with high accuracy, but find their predictive ability is decreasing in LOVE. Additionally, we demonstrate that LOVE-driven trading results in long-term financial losses for the average investor, highlighting a trade-off between wealth and emotional utility. Our study pro- vides novel economic insights into the factors shaping emotional dividends and their role in the pricing of private-value assets. It also surfaces the limitations AI faces in emotionally charged markets, revealing new challenges for algorithmic trading when assets carry significant emotional utility.