Introduction
Music streaming is the primary form of music consumption today, with major streaming platforms such as Spotify having over 700 million active listeners as of the end of 2025, and AI plays a huge role in this industry. Despite being more commonly known for generating songs and imitating artists, AI's most influential role today is within streaming services such as Spotify, Apple Music, and YouTube Music. It enables these services to search their vast libraries, personalise recommendations, and enhance the music user experience. Because of its significance, AI not only supports music consumption but also mediates the relationships among listeners, creators, and the industry.
One of the main reasons streaming services rely on AI is scale. The sheer amount of tracks uploaded to platforms every day is just not feasible for humans to manually sort through. For instance, approximately 100,000 tracks are uploaded daily to Deezer, a popular streaming service. Without automated systems, categorizing, moderating, and recommending this number of songs is not a realistic task. AI allows these services to operate efficiently while maintaining personalised experiences for their millions of users simultaneously.
Personalization
To be able to provide a personalized experience for all its users, AI analyses a large amount of user listening behaviour. This includes things like the genres of music a user typically listens to, which songs they tend to skip or replay, their retention with different tracks, and how these habits change over time. In addition to behaviour, AI also analyses the music itself. By interpreting the musical features of songs, suhc as the tempo, rhythm, and energy, AI can identifiy similarities between songs and categorize them accordingly. This is further enhacned with Natural Language Processing (NLP). Using this model, AI can find patterns within the textual content of the songs, meaning its descriptions, lyrics, and user-generated tags. Using the infomation it gathers, AI can infer the mood, genre and theme of each song. Rather than understanding the music in a human sense, AI predicts what a user is most likely to enjoy using patterns observed in their habits.
These systems are most visible through recommendation features. Personalised playlists like Spotify’s Daily Mix or AI-generated playlists based on user prompts (e.g. “relaxing music to tide me over during allergy season”) are direct outputs of algorithmic decision-making. Autoplay functions, song radios, and ranked search results further guide listening behaviour, often without users consciously noticing.
Engagement
Streaming services often also use AI to create user engagement, fostering a stronger community within their users. A well-known example of this would be Spotify Wrapped. Spotify Wrapped is a feature Spotify has for its users, where at the end of each year, the system summarizes the songs and artists the user listened to most in that year. They then encourage the user to share their list on social media, allowing other Spotify users to have a look and compare it to their own summary, thus creating engagement. This simultaneously generates excitement around Spotify, acting as a natural advertisement for the service.
AI's use in advertising streaming services goes even deeper, however. Streaming services such as Spotify utilize AI to do targeted advertisements. Using the a user's information, AI determines the visual and audio aid that would be most effective for each user, showing an advertisement accordingly. This information includes the user's listening patterns, demographics, location, and more.
Concerns
Despite these benefits, there are clear limitations and ethical concerns associated with AI-driven streaming. Recommendation algorithms often reinforce existing preferences, creating feedback loops that prioritise familiar or popular content. This popularity bias can reduce musical diversity, making it harder for unconventional or emerging artists to gain exposure. Much like AI music generation models trained on narrow datasets, streaming algorithms may struggle to promote diversity if they are optimised primarily for engagement rather than exploration. Additionally, when AI involvement is hidden, listeners may feel deceived, contributing to distrust toward AI-generated or AI-curated content.
Conclusion
Ultimately, AI has transformed music streaming from a passive listening experience into an algorithmically guided one. It shapes what music is discovered, which artists gain visibility, and how cultural trends evolve. While AI offers undeniable benefits in terms of efficiency, accessibility, and personalisation, it also raises important questions about fairness, diversity, and transparency. As AI becomes further integrated into streaming platforms, the challenge will be ensuring that these systems support a healthy musical ecosystem, one where technology enhances human creativity rather than overshadowing it. To find out more about how AI can assist human creativity, read "AI and Human Creativity."