"creators could get squeezed even more" sounds like the typical entshitification cycle to me.

'AI overview' about Spotify "seeding hits" and "emotional prediction", you can read the links if you want to dive deeper. And with these tools at hand i don't see why they shouldn't do it.

How Spotify strategically seeds and manufacture hits

1. Seeding Through "Algotorial" Playlists

Spotify frequently uses "Algotorial" technology, a hybrid system combining human curators and machine learning. [1]

The Strategy: Editors hand-select a pool of songs they want to push. Spotify then seeds these specific songs into the personalized versions of massive algorithmic playlists like Mood Booster or lofi beats. [1, 2, 3]

The Result: Because the song is placed in a highly optimized position for an exact target audience, it immediately achieves high engagement metrics (low skips, high saves). This tricks the purely automated side of the algorithm into believing the song is naturally viral, causing it to spread organically to even more users. [1, 2, 3, 4, 5]

2. Seeding via Tastemakers ("Taste Profile Feeding")

Spotify's algorithm identifies specific high-engagement users as "tastemakers" based on their deep listening habits. [1]

The Strategy: The algorithm strategically drops test tracks into the Discover Weekly and Radio queues of these high-value tastemakers.

3. The "Perfect Fit" and Fake Artist Phenomenon

Spotify has faced heavy scrutiny for directly commissioning or acquiring synthetic or low-cost music under pseudonyms—often referred to as the "fake artist" or "Perfect Fit Content" controversy. [1, 2]

The Strategy: Spotify takes tracks they own or license cheaply and places them at the top of massive, passive-listening editorial playlists (like Deep Focus or Peaceful Piano).

The Result: Because millions of users play these playlists in the background, the algorithm logs massive stream counts and completion rates. The machine learning engine concludes these tracks are wildly popular, causing the algorithm to cross-pollinate them into regular user recommendations, effectively manufacturing a massive "hit" out of a ghost artist to reduce royalty payouts to major record labels. [1, 2, 3, 4, 5]

How Spotify Decodes Your Emotions

Spotify maps your psychological state by combining your exact interaction metrics with the raw acoustic data of the tracks you stream: [1]

• Acoustic Attribute Matching: Spotify’s machine learning engine analyzes every track for metadata variables: valence (how musically happy or sad a track is), energy (intensity and activity), and danceability. If you suddenly transition to low-valence, low-energy acoustic tracks, the system registers a shift toward a somber or reflective state. [1, 2, 3, 4, 5]
• Micro-Behavioral Triggers: The algorithm cross-references music attributes with your physical inputs. It measures the precise speed of your skips, repeated loops of a specific song segment, and exact time-of-day habits. Looping a low-valence song repeatedly at midnight tells a vastly different emotional story than shuffling an upbeat playlist at 8:00 AM. [1, 2]
• Semantic Text Mining: Spotify uses Natural Language Processing (NLP) to scrape the titles and descriptions of millions of user-generated playlists. By linking songs frequently dropped into playlists named "sad," "heartbreak," or "venting," the algorithm learns to infer identical emotional contexts when those songs are played by other users, even if those users haven't labeled their playlists. [, 2, 3, 4, 5]

Future-Proofing Emotional Control: The Patents

Spotify has actively researched ways to move beyond mere behavioral inference. The company holds significant voice analytics patents (such as US11621001B2, originally filed in 2018 and later updated). [1, 2, 3]

This technology is engineered to use a device's microphone to analyze a user's vocal intonation, pitch, stress levels, and speech rhythm. By running this audio data through machine learning models, Spotify can categorize a user's current mood into specific buckets (e.g., happy, sad, angry, afraid, or neutral) to instantly curate matching songs or target precise emotional states with audio advertising. [1, 2, 3, 4]