In the Sci-Fi movies of old us humans were regularly subject to being probed and analyzed by the aliens. Today, this job has been handed over to the algorithms. Our every digital move is being observed and processed in various ways with various underlying objectives. In any walk of life where understanding human motivation and behavior has a commercial and/or organizational benefit, the algorithms are working overtime!
How The Digital Service Providers employ AI Algorithms
DSPs like Spotify, Apple Music, YouTube Music, Tidal etc. rely heavily on personalized recommendations to keep their user base content. The algorithms have vast amount of user data to “chomp down” on and digest into personalized music recommendations.
Your listening history (played songs, dis/likes, search data, playlist interactions), demographic data (age, location, devices used, listening data, and time) and other data like genre preferences, your social data (if connected), and, in some cases, mood.
The level of data is naturally subject to each individual’s privacy settings, but most users are fine with the data sharing as long as the music recommendations are precise and keep introducing users to new artists and songs they might not have encountered otherwise.
AI Curation: Playlists and Social Connections
AI-powered playlists are getting listeners hooked on the service by curating songs based on specific themes, moods, or activities. This “passive way” to discover new music through personalized playlists tailored to their tastes, generally leads to increased engagement and longer listening sessions.
AI algorithms are also used to analyze social connections and interactions on music platforms, suggesting songs based on what friends or similar users are listening to. This is a technique to create a sense of community previously lacking on these platforms and to facilitate music discovery through shared tastes.
How is the Secret (AI) Sauce Made? Music discovery and AI
Machine Learning (ML) techniques are leveraged to learn from the patterns and trends in the data and develop predictive models to optimize personalization. ML is a subfield of artificial intelligence that focuses on enabling systems to learn and improve from experience without being explicitly programmed.
Nature Language Processing (NLP) takes textual data (for instance reviews or ratings) to analyze the user’s interests and sentiments. NLP focuses on enabling computers to understand, interpret, and generate human language in a way that is both meaningful and useful. It combines computational linguistics, machine learning, and deep learning models to process and analyze large volumes of natural language data Together, these subfields of AI are used to create personalized recommendations for users.
Back to the Future? The Impact on Music Consumption Habits
One clear effect of AI-led consumption is a return to the single-track domination of the pre-album era. When “pop music” became a cultural phenomenon the principal artifact was “the single”. Artists were vying for radio play by trying to pen the latest “hit single” for the radio. In 1946 Frank Sinatra released “The Voice of Frank Sinatra”, generally considered the first “popular music album”.
Almost 20 years later The Beatles (1965) released Rubber Soul, and the format became de rigour for pop artists who wanted to create a cohesive artistic statement on vinyl. Led Zeppelin took it to the next level by refusing to release singles at all, a decision which paid off as their fans bought their albums as if they were singles.
When the first CDs hit the market in 1982, this, to a degree, marked the beginning of the end for the album format. The digital format spawned “the MP3 revolution” that would prepare the ground for digital platforms. And, even though the CDs released continued the album format tradition, the extra space available meant that artists produced overly “bloated” releases, which led to fans wanting to cut away the dross and burn CDs or download MP3s with favorite tracks only.
Cut to today, with personalized recommendations and algorithmic playlists driving listening praxis, users are consuming individual songs rather than full albums. Artists and labels are scrambling to accommodate this return to a single-track-based market.
Arguably, pop music has always been focused on the single, though artists like Michael Jackson and Prince certainly made a case for the album. And, many artists feel that they best express their art through the cohesive album format. Despite surging vinyl sales, it is hard to see this changing soon, and artists need a strategy.
This strategy doesn’t need to undermine The album, but staying in the conversation with releasing collabs, remixes, live tracks etc. are ways to “feed the machine/algorithms”. I believe in approaching this as a creative challenge rather than a dynamic that is detrimental to artistic expression.
Next effect: The filter-bubbles
While AI algorithms offer a wealth of personalized recommendations, there’s also a risk of users being trapped in a “filter bubble” where they are only exposed to music that aligns with their existing preferences. This could limit their exposure to diverse genres and artists, muting musical exploration and discovery.
Maybe you are fine with listening to the same hair-metal bands you rocked out to in 1985, or the acid-house tracks you partied to at raves in 1992, but if you want to step out of the time-machine, users need to let the algorithms know!
Artists can’t really create a strategy for “filter bubbles”, but can reap surprising rewards when their back catalogue suddenly fits a nostalgia trend and listeners rediscover their music, and become (re)introduced to their “bubbles.”
Positive Vibes, then? The “try this” effect.
AI-powered recommendations and playlists often boost user engagement and music consumption, as users discover and enjoy more music that aligns with their tastes. The “if you like this, then try this” dynamic is strong across all e-commerce businesses, and particularly powerful in guiding your listening habits.
As an artist, “surfing the recommendations waves” will naturally generate more streams and more revenue. Again, there is no clear-cut strategy to becoming an artist that digital platforms like to push to their users, besides making sure that your tracks are uploaded with optimal metadata and to be productive with adding content on each platform.
Coda
For better, or worse AI algorithms are playing an increasingly significant role in shaping how people discover and consume music. It is easy to get sentimental and idealistic about how this works in the music industry. However, a deeper understanding of how AI is “governing” consumption and discovery of your art is only going to help your career. And, If there’s one thing we know about the music industry, it’s that tomorrow will look completely different!
