What is the intuition behind separating hyperplanes in high-dimensional space?

In high dimensions, data often becomes more linearly separable due to feature expansion.

As dimensionality increases, points become more spread out, making separation easier. SVM exploits this by finding hyperplanes that maximize margin even in very high-dimensional feature spaces induced by kernels.