When I open up Spotify to listen to my favorite music, I face a dilemma. I have access to a nearly unlimited selection of music at my fingertips, ranging from a remastered version of a Smiths song to Japanese hyperpop to a Bob Marley reggae jam. With the dominance of streaming services like Spotify and Apple Music, artificial intelligence has emerged to help solve this issue: AI-generated playlists have become hyperspecific, catering to specific moods, scenarios and times of the day. Why should I carefully curate personal playlists when I can turn to a formulated Spotify “Cooking Mix” or “Cleaning Mix” that knows exactly what I want to listen to based on my listening history?

Spotify Discovery Mode is a new marketing tool that helps artists utilize artificial intelligence to reach a larger audience by analyzing certain markers such as audience retention and whether people shared a song or added it to a playlist. Spotify uses this tool as an opportunity to take more of a cut of artists’ royalties in order to serve their music to more people. The move toward AI-generated playlists presents a concerning shift in how people go about listening to and producing music.

In response to these changes, artists have even started to create music to appeal to algorithms or TikTok trends, increasing repetition and shortening the lengths of songs. As a result of streaming service methods, mainstream artists are incentivized to create music that doesn’t sound particularly interesting or provide anything unexpected in favor of a higher likelihood of success within music algorithms. The algorithms used by streaming services like Spotify and Apple Music base their algorithms on data and user behavior, which means they are inherently biased toward certain demographics and types of music, leading to a homogenization of musical tastes. Despite these concerns, streaming algorithms do provide the benefit of allowing listeners to discover new music. However, listening to music is a creative and personal experience, and features like Spotify’s Discover Weekly box listeners in with a reduced, marketable version of a mood or a genre. 

Kenyon has an especially robust culture around original music performance through venues such as the Horn Gallery, which transcends the mundane nature of engaging with music as shaped by music algorithms. In the age of streaming, a local band could have thousands of fans in Germany, but be relatively unheard of in their hometown. Art’s popularity should not be decided by engagement or listening data as decided by an algorithm, but rather by what is simultaneously pleasant but also challenging. Listening to music that strays from common themes, production style or format is ultimately more intellectually challenging — but lends itself to the possibility of a more fulfilling listening experience. 

As listeners of music and members of a community filled with artists, it is our duty to go looking for the exceptional or the unusual. While algorithms speed up music consumption and discovery, they also limit meaningful user engagement with music. We should attend more live performances, support local bands and engage with music in a more intentional and mindful way. Ultimately, it is up to us to decide how we want to listen to music and to resist the homogenizing tendencies of algorithmic playlists. Streaming services might seem as if they have an uncanny feel for our tastes, but they prevent us from cultivating a personal relationship with music and listening to what we really want to hear. It’s time to start listening to ourselves instead.

Dylan Sibbitt ’26 is a columnist for the Collegian. He is a political science major from San Francisco. He can be reached at sibbitt1@kenyon.edu.