Solving optimal transport (OT) on random minibatches is a common surrogate for exact OT in large-scale learning. In flow matching, this surrogate is used to obtain OT-like couplings that can straighten probability paths and reduce numerical integration cost. Yet, the population-level coupling induced by repeated minibatch OT remains only partially understood. We formalize this coupling as the expected batch OT plan, obtained by averaging empirical OT plans over independent minibatches. We then establish its large-batch consistency and, in the semidiscrete case relevant to generative modeling, derive rates for both the transport-cost bias and the convergence of the expected batch OT plan to the OT plan. For flow matching, this yields a population coupling whose induced velocity field is regular enough to define a unique flow from the source to the discrete target. We finally quantify how OT batch size interacts with numerical integration in a tractable two-atom model and in synthetic and image experiments. This talk is based on a joint work with Samuel Boïté and Julie Delon:https://arxiv.org/abs/2605.12174