You can’t always do that because you often don’t know how many pseudorandom numbers you will need. Search for probabilistic computational linear algebra for more, but say you’re trying to do research into genetic conditions. Your data might be a matrix of samples vs genes and each cell a record of how much a gene is expressed in a certain sample. So you have a very big matrix that you need to do a singular value decomposition. The standard way to donthis involves random sampling of the columns to make the computational complexity manageable. You would still want to seed the rng so your results are reproducible.