Soon Your AI Assistant Will Do the Shopping — And Catch the Ads

A new position paper argues that recommender systems are undergoing a bifurcation. The old assumption — that a human directly consumes each recommendation — is challenged by AI agents powered by large language models, raising a central question: who is the receiver of a recommendation? In delegable contexts, such as routine purchases, travel, and constrained transactional tasks, the primary operational consumer of recommendations is shifting from the human to the agent, requiring new optimization objectives, interaction protocols, and evaluation criteria. In experiential contexts, such as entertainment, art, and other subjective or high-stakes choices, humans remain the final judge of relevance, though agents may assist through pre-filtering and curation. The authors introduce a delegation spectrum characterizing recommendation contexts along factors such as preference specifiability, outcome verifiability, and decision stakes, and outline a research agenda spanning agent preference modeling, dual-audience optimization, and the emerging agent attention economy.