The "law" part of scaling laws is about predicting validation cross-entropy loss from the training configuration, analogous to physical laws allowing to predict

The "law" part of scaling laws is about predicting validation cross-entropy loss from the training configuration, analogous to physical laws allowing to predict one quantity based on the measurement of another. Most scaling laws take the form of an irreducible error plus additional terms that asymptotically decay to zero. So that there is a wall you can approach but not cross (the irreducible error) is an integrated part of the scaling law paradigm. That it isn't economical to keep increasing model size to squeeze out a few more drops of cross-entropy doesn't mean scaling laws stopped working. Strictly speaking, "Why do scaling laws work?" is a question about the theoretical reasons the asymptotic decay takes the particular mathematical shape that it does.