[revision17]

In addition to what others say said, computational complexity, is a big reason. Gaussian Process and Kernelized SVM have fit complexities of O(n^2) to O(n^3) (where n is the # of samples, also using optimal solutions and not approximations). While Neural Nets and Tree Ensembles are O(n).

I think datasets with lots of samples tend to be very common (such as training on huge text datasets like LLMs do). In my travels most datasets for projects tend to be on the larger side (10k+ samples).