The “no-free lunch theorems“ essentially say that for any two algorithms A and B, there are “as many“ targets (or priors over targets) for which A has lower expected loss than B as vice-versa. This can be made precise for certain loss functions [WM97]. This note concerns itself with cases where seemingly harder matrix versions of the algorithms have the same on-line loss bounds as the corresponding vector versions. So it seems that you get a free “matrix lunch“ (Our title is however not meant to imply that we have a technical refutation of the no-free lunch theorems).