Finding a new artist through Spotify’s Smart Shuffle system always feels like a great personal discovery because of how refreshing it feels to branch out from the same old music.

And yet, discovering a random old song from a popular artist that you’ve never heard of before — a common personal experience — acts as an illusion of a breakthrough. This illusion falters as you realize that any number of other people have received the exact same recommendations from Spotify. What feels like intricate personalization is actually just generalized suggestions wrapped in minimalist aesthetics.

Apps like TikTok and Spotify that offer to help you hone your taste instead act as retention machines. They don’t really care about what you’re listening to, only whether or not you’re using the service. Such companies make a mockery of true taste curation and personalization.

A single week of listening to house music is enough for a month’s worth of generated Discover Weekly playlists on Spotify of just that genre. The platform doesn’t provide variety, instead solely basing itself on the assumption that you want to be directed to music you’ve already shown interest in. 

These algorithms aren’t reading minds, but are instead crunching numbers on patterns. For instance, Discover Weekly relies heavily on collaborative filtering. This means that its algorithm looks at what people with similar listening habits to you are streaming and recommends those same songs. 

While this process theoretically makes sense, in actuality it can act like a mirror reflecting your own taste. The same logic applies to TikTok’s “For You” page where the system, marketed as discovery, is actually optimized for retention rather than exploration in a time sorely lacking curiosity. 

Between classes, while hanging out with friends or in line for Cafe Dulce at USC Village, we’re  constantly feeding the algorithm data, making college students the almighty algorithm’s poster child. 

College is meant to be four years of discovery, whether that be through new friends who drag you to places you normally would’ve never gone or introduce you to newfound interests. That’s the whole point of being surrounded by thousands of people who grew up in different environments than you.

However, algorithms work actively against those differences: Finding a book recommendation in a class is miles more refreshing than being recommended the same book on TikTok over and over again because real-life discovery feels organic. You remember the things you touch or the things you receive from people you care about. Un-algorithmic, or natural, discovery is the whole point of college, but is being crowded out by predictive programming.

However, it is important to note that algorithms aren’t wholly negative. They can help with initial discovery and with finding a new community that enjoys your niche interests. 

Our generation’s complex relationship with computerized discovery raises an even deeper question: Is there such a thing as bad discovery? 

At the end of the day, you’re still finding something new, just hyper-specifically tailored to you. The entire framework stays within a system that is already predicting what you might like. 

While algorithmic discovery proliferates, organic discovery doesn’t necessarily disappear; rather, it gets placed in a smaller, finite field of possibility, despite its seeming infiniteness. 

Maybe the fix isn’t deleting TikTok or swearing off generated playlists forever; it’s remembering that algorithms are not actually mapping anything new, just creatively redrawing the blueprint we already know. 

Nonetheless, real discovery isn’t dead, just slow, and it doesn’t come with a “For You” label. It looks like taking a general education class distant from your major, joining new clubs, organically meeting people with distinct interests or simply asking friends for recommendations. In an algorithm-centered world, it’s important to remember that real personalization simply can’t be predicted.