For decades, editorial decisions and news media organizations’ strategic goals have primarily determined the distribution of journalistic content. However, the integration of artificial intelligence (AI) into journalism has significantly altered news content selection in an increasingly digital news media environment. At the same time, there has been a shift toward algorithm-based news recommender systems (NRS) that automate and personalize news content recommendations. While AI presents opportunities to enhance audience engagement and, therefore, higher advertising revenues through hyper-personalized news recommendations, the concept remains underexplored in journalism research. Against this background, this chapter examines the potential of AI-driven hyper-personalization in journalism, analyzing its benefits and challenges. It reviews existing literature on ethical and practical implications of news content personalization and recommendation, while also exploring emerging application areas of AI-powered news recommendations, such as predictive analytics and dynamic paywalls. Additionally, the chapter considers the risks associated with a loss of editorial control and the audience perspective. This chapter contributes to the ongoing debate on AI-driven content personalization and recommendation by providing a comprehensive analysis of AI’s role in hyper-personalization, arguing for the need to integrate AI in the news industry responsibly.