“I know what you listen to, what you like, what you skip.”

That’s how Spotify’s AI DJ X introduced itself to me recently — and to be honest, it was slightly unsettling. At that moment, I re-acknowledged what I already knew and often quietly pondered about. The AI DJ wasn’t just casually recommending songs; it was actively learning, archiving, and predicting my mood before I fully understood it myself. It reminded me I’d listened to a particular track 47 times in June 2023 — as though suggesting, “You liked it then, you’ll like it now.” I did listen. Nostalgia washed over me and I loved the song yet again. However, I couldn’t shake the feeling that I hadn’t chosen to remember, but was made to remember. In that instant, AI decided my mood.

Despite these reservations, I sometimes let it play my songs. Some days, it gets it eerily right. Other times, it tracks my skips and gently nudges me to give it direction. It often leaves me wondering — did it truly find the perfect track for my mood, or subtly tune my mood to fit its selection? What was once a spontaneous ritual of discovery is now increasingly shaped by a machine designed not just to anticipate my preferences, but to steer them.

At first glance, AI-curated playlists feel like harmless convenience — a bespoke soundtrack tailored to your taste. Yet beneath that ease lies a quiet reshaping of choice. Music has always reflected identity, community, and rebellion. It’s how we narrate our lives to ourselves and sometimes to others. When algorithms intervene, ‘taste’ risks becoming a closed loop, dictated by our past preferences and by what’s most commercially viable.

The AI DJ’s job is to reduce dissatisfaction, and the simplest way is to keep listeners within the safe bounds of their familiar favourites. While this means fewer frustrating skips, it also narrows our exposure to new genres, emerging artists, or tracks that challenge our usual tastes. The joy of stumbling upon something unexpected is harder to come by when playlists are optimised for predictability.

The wider cultural implications are significant. AI-driven content recommendation systems — in music, films, and news — shape trends by amplifying what’s already popular and quietly sidelining what isn’t. On the surface, the idea of homogenising public taste might seem far-fetched. After all, we’re unlikely to wake up one day and find only two artists dominating global charts. What’s more plausible is a gradual narrowing of visibility, where newer, experimental, or niche artists find it increasingly difficult to break through. In a system designed to prioritise commercially proven, algorithmically popular tracks, the margins grow quieter.

Choice, however, isn’t lost — it’s simply easier to forget we have it. Algorithms learn from the patterns we offer them. Each time we skip a song, seek out a lesser-known artist, or play something unexpected, we disrupt the feedback loop. It’s a small reminder that human taste is inconsistent, unpredictable, and gloriously erratic. The intention here isn’t to reject AI DJs altogether. For many, comfort in the familiar is a perfectly valid pleasure. Yet it’s worth engaging with these tools consciously, recognising when convenience risks becoming quiet conformity. Sometimes, let the AI guess your mood. At other times, choose differently — not in protest, but in the quiet, personal act of steering your own soundtrack.

After all, music has always found a way to slip through the cracks. New sounds, underground movements, and strange subcultures have survived far from the mainstream gaze. Perhaps AI will never fully grasp the ineffable reasons a song moves us — and maybe that’s what will keep music, and choice, beautifully human.

The writer is an enthusiast of Art, Media, Technology, and Culture, with a keen interest in how these four domains influence one another, creating ripples in society and human behaviour. In addition to her focus on technology, Sayanee writes extensively about art, history, food, and media, examining their cultural significance and their role in shaping social identities and practices.