Our generation seems to be facing a crisis of critique. We want to know what's best, we want to know where to eat and what movie to see, but we've begun to forget that real opinion, real critique, must always come out of an absence of voices–from a singular subjective viewpoint. You cannot aggregate taste. But in the flood of rating systems and collectivized percentage values, which guide us toward TV shows on Netflix or songs on iTunes, we don't register the loss of that less aggressive suggestion system we always relied on before: face-to-face encounters and singular critics.

If sites like Pitchfork offer coolness as aspirational, Spotify suggests users are already cool enough. Spotify doesn’t have to prove it is a tastemaker you should follow. Instead, it flatters: Your taste is already great, and here’s some tracks to show how tasteful your listening data has already proved you are. In Spotify, taste requires no effort, no cultural capital, no imaginative synthesis or rationale. It becomes simply empirical, latent in the data and waiting to be extracted by Spotify’s algorithmic analysis.

Search and recommendation algorithms can be pretty effective at surfacing content a user already likes, but they're not very good at exposing an individual to content she doesn't like, or content she doesn't even know she likes. The issue comes down to a basic push-pull concept in machine learning, called exploitation vs. exploration – the exploitation side is surfacing what a user knows and loves, the "go-tos," whereas the exploration side is what it sounds like, discovering new things you like

All the products you use, from Google search to Facebook to Spotify, try to predict what you want in real time, whether it's an image that will intrigue you, a hard news story, a music video, or an uncle's Facebook rant. It forces content creators, especially those who want to appeal to as many people as possible, to play by the algorithms' rules, and no one's immune. Your media becomes a product of your existing tastes, as filtered through a tech company's assumptions about what you already like.