Why does high dimensionality make optimization landscapes ill-conditioned?

Because eigenvalues of the Hessian matrix spread widely.

In high-dimensional optimization problems, the Hessian often has a large condition number (ratio of largest to smallest eigenvalues). This creates narrow valleys and flat plateaus in the loss landscape, making gradient descent inefficient. The optimizer oscillates in steep directions while progressing slowly in shallow ones.