Early research suggested a predictable relationship where increasing a model's size and training data led to smoothly improving performance. However, some recent, extremely large models have demonstrated capabilities and performance gains that were not perfectly predicted by these initial mathematical models. Critically evaluate two potential interpretations of these findings:

1. The initial principles describing the relationship between scale and performance are fundamentally flawed and must be discarded.
2. The initial principles are a solid but incomplete foundation, and they must be refined to incorporate other crucial factors (such as data quality, model architecture, or training methods) to create a more accurate model.

In your response, justify which interpretation is more scientifically robust and describe the type of experimental evidence that would be needed to strengthen your argument.