Approximations to Wellbeing is a conceptual DJ mixtape investigating how technologies of automation and affective design intersect in the production of artificial comfort. The work takes as its point of departure the corporate and algorithmic pursuit of well-being as an emotional commodity—how brands, apps, and platforms engineer states of numbness, calm, or “flow” to render users more receptive to marketing, consumption, and data extraction.

Each track was generated with Boomy, a machine-learning platform that allows users to specify only general expectations (“happy,” “lo-fi,” “chill”), leaving the system to fabricate an affective sound environment. The resulting playlist was then mixed autonomously by Pro Djay, a neural DJ app that analyses harmonic key and tempo to produce smooth, unbroken transitions. The mix follows a strict format: every track plays for 30 seconds before the next begins—120 tracks per hour—a continuous, placid stream of generically “pleasant” audio.

The title refers ironically to the corporate aesthetic of “well-being,” which functions less as care than as control. Like Brian Eno’s Music for Airports, the mix juxtaposes sound and site: music that evokes tranquility against the background of machinic noise, exhaustion, and circulation. Yet here, the affective smoothing is total—machine-produced, algorithmically optimized, and stripped of authorship. What remains is a simulation of emotional balance built on computational repetition.