Motian in Motion celebrates the ECM artist as he drums his way through the Village Vanguard, Birdland, the Blue Note, and beyond. Halfway through Motian in Motion, a […]
Anna Butterss vs. the Algorithm: The Case for Curated Music Discovery

Listen to the bassist’s new album for International Anthem while reading a brilliant Tiffany Ng essay on curation.
“As we grow accustomed to the convenience of shuffling a generated playlist, we forget that discovering music is an active exercise.”
This sentence was recently penned by writer Tiffany Ng in an illuminating essay about Spotify’s discovery engine. Called “How to Break Free of Spotify’s Algorithm,” the piece breaks down in brilliant detail the ways in which Spotify and other streaming services feed their subscribers new music. Subtitled, “By delivering what people seem to want, has Spotify killed the joy of music discovery?,” Ng’s piece was published by the MIT Technology Review.
While you read it, maybe think about using as your soundtrack Anna Butterss’ new album, Mighty Vertebrate.
It’s the bassist’s second solo album, and first for International Anthem. If you know the work of Butterss, it’s maybe through their remarkable work with Jeff Parker during their regular Monday gig as the Jeff Parker ETA IVTet — recordings of which were culled to create his album Mondays at the Enfield Tennis Academy — and the forthcoming follow-up. Butterss is also a member of SML, whose 2024 debut album, Small Medium Large, is one of our favorite of the year.
Anyway, in Ng’s essay, she focused on what she calls Spotify’s “game-changing release in 2015 of Discover Weekly — a generated playlist that tailors song selections to a user’s listening habits,” noting that the company “presented personalization as the remedy to our overabundance of options.”
The problem? AI is crucial for Spotify’s discovery mode, and its logic and approach is flawed when it comes to discovery.
[I]n efficiently delivering what people seem to want, it effectively eliminated choice and removed humanity from the entire music listening—and music discovery—experience. According to a 2022 report published by Distribution Strategy Group, at least 30% of songs streamed on Spotify are recommended by AI. The success of Discover Weekly has since inspired mood-dependent playlists that change throughout the day and psychic readings based on people’s listening habits. Other streaming platforms, like Apple Music and Amazon Music, have followed suit. All these takes on personalization share a common fault: The playlists too often resemble one another, filled with songs that offer different variants of the same sound.
Which is to say, whether or not you’ve happened upon an artist such as Butterss’ work on one of your streaming platform’s discovery playlists is a question of mathematics, not curation. Too, even if their work does show up on a playlist, it does so minus any context. Just a name and a track devoid of information, one that requires that listeners resist the temptation to skip the song if the first few seconds don’t catch their attention. That’s far removed from learning about an artist or album from a friend, record store or music blog.
Ng: “Because personally recommending songs revealed our taste, we had a vested interest in what we recommended. But the algorithm assumes no risk, simply offering what’s mathematically sound.”
Butterss made their new album in a very precise manner, they say in release notes to Mighty Vertebrate.
“I had just gotten off of a bunch of touring at the end of 2022 and just wanted to write music,” says Butterss. “The best way for me to do that, I’ve found, is to set myself a discrete and focused task.” The prompts read like something out of an Oblique Strategies deck:
I’m going to make a song where the bass doesn’t function in the role of a bass.
I’m going to work on this for an hour and then I’m going to stop.
I’m going to make a song that uses groups of three-bar phrasing.
I want to sample something and make it into a song.
I’m going to start with a drum machine.
“Every song was like that,” Butterss continues. “Then once I got started I just followed where my mind wanted to go. It was very structured.”
Structure. In Ng’s piece, she offers an overview of various structured human communities that have risen to combat the reductionist tendencies of Spotify algorithms. Her conclusion is an idea that’s foundational to In Sheep’s Clothing’s mission.
Perhaps the only way to escape our algorithmic bubbles is by building community. When we welcome diverse patterns of music consumption, we’re challenged to consider music from different perspectives, the same way independent radio stations curate to tell a story rather than cater to a demographic. There’s nothing to optimize in a community, and in turn, nothing to oversimplify.
Despite functionally contradicting Spotify’s philosophy, platforms like Radiooooo, Music League, Oddly Specific Playlists, and independent radio all complement the use of such platforms. They act as a springboard for our process of discovery, helping us step past Spotify’s insistence on personalization by directing us where to look and, most important, making it fun.
Read Tiffany Ng’s essay in its entirety at MIT Technology Review. Grab a copy of Butterss’ Mighty Vertebrate over at International Anthem.