I think that's a tempting conclusion.

From my perspective, parallelisation might be useful, but only in a form that scikit-learn doesn't readily support, such as vectorize the data contained on k different machines locally, then merge the matrices. Providing tools to merge the matrices might be useful (and I have some coded up somewhere), but then we require a motivating example, etc...