Why do neural networks develop similar internal patterns regardless of how they're trained?

Networks trained on the same task with different architectures or datasets spontaneously develop aligned internal representations. The alignment depends on signal-to-noise ratio and training set size in unexpected ways: alignment actually dips near the point where networks memorize training data perfectly, and better alignment doesn't guarantee better predictions. This decoupling suggests alignment and generalization are governed by different forces.