I Can Tell You're About to Quit, Just from Your Liked Playlist.

It sounds like an urban legend, but it's true. Two years ago, the company launched an "Employee Wellness Program," one perk of which was subscribing to an enterprise-grade music streaming service for us to relax while working. You had to log in with your company email, and you could even share your musical taste through department playlists. It seemed genuinely considerate.

Until six months ago, the head of the big data department called me into a meeting room and locked the door. He showed me a correlation analysis: "Look, this is the listening data from the tech department's employees who left in the first half of the year, for the three months before they quit." The charts clearly showed common patterns: A sudden increase in listening hours during late-night overtime periods; the emotional tags of their saved playlists drifting from 'Calm' and 'Focused' towards 'Cathartic' and 'Agitated'; crucially, they started heavily listening to very personal songs not on any 'company shared playlists'—as if drawing a digital boundary in advance.

"Our 'wellness' perk might be the best early warning system," my boss lowered his voice. "I want you to build a model. Don't touch emails or chat logs. Just analyze this 'public' music data. Let's create an 'employee psychological weather forecast'."

I started working like a digital-age shaman, deciphering the code behind the melodies. The model didn't focus on the songs themselves, but on the "patterns of change":

The "Isolation" Signal: An employee who only ever listened to the company's "Focus Coding" public playlist suddenly starts consistently saving late-night jazz and obscure indie rock. This might not be a change in taste, but more likely them rebuilding a private mental space "unrelated to the company." It's a form of digital resignation.

The "Energy Curve" Collapse: A salesperson's data used to show they'd loop high-energy electronic music during project sprints. But after a certain point, their playlist became dominated by slow, sad, or nihilistic Lo-fi. This often isn't fatigue, but a deeper breakdown of motivation—the energizing music no longer works because what they need isn't energy, but solace or numbness.

The "Late-Night Tag" Carried the Highest Weight: If someone repeatedly listens to angry, defiant anthems after 10 PM, or puts a particularly farewell-themed live version on repeat, the emotional density in that is a hundred times more real than any anonymous satisfaction survey.

I first felt the model's cold power when analyzing "Old Chen's" data. A ten-year veteran in ops, universally seen as stable. But my model flagged him as "High Risk" for three consecutive weeks. The data showed: He used to listen to classic, steady oldies, but recently, his late-night playlists were suddenly filled with dissonant, psychedelic rock—songs full of alienation and absurdity. I submitted the warning as per procedure.

Three days later, Old Chen calmly announced his resignation in a weekly meeting, saying he "had an epiphany that life can't just be about being a component maintaining system stability." In that moment, everyone else in the room was surprised. Only I knew his playlist had sounded the alarm weeks prior. His spirit had already checked out long before his body did.

Of course, the company never admits to monitoring. In an internal meeting to secure more budget for this project, the conversation was clinical:

Me: "The emotion analysis model based on music data has reached an accuracy rate that can assist in judging employee engagement trends."

HR Director: "Good. But don't use words like 'monitoring' in the report. Call it the 'Proactive Employee Care & Insight System.' We're doing this to care for our employees, right?"

Legal Liaison: "Correct. All data is from a voluntary benefit. We analyze anonymized trends. The results are merely 'reference.'"

VP: "Build a 'Warning-Intervention' loop. For high-risk employees, maybe auto-suggest EAP counseling? We need to showcase the warmth of technology."

You see, even the "intervention" is designed to be beyond reproach. We've built a glass prison based on melodies. The guards can't see the inmates, but they can hear every sigh or roar from their inner worlds, and call it "care."

Now, the first thing I do every day is check the dashboard. I watch the emotion curves next to anonymous IDs. I know that when a curve starts to violently deviate from the "company mainstream," a resignation letter is likely already on its way.

Sometimes, I open that company music app myself, wanting to listen to something. But my finger hesitates. I can't help but wonder: how will the model interpret this song? As "Stable," "Anxious," or a potential "Betrayal"?

I've become someone who spies on secrets through playlists, and because of that, I can no longer find my own, carefree immersion in any melody. The music is gone. All I hear now is data.