Models that turn what a robot sees into what a robot does have to be retrained constantly, because every simulator, every robot body and every task objective is a little different. That retraining has to run somewhere, and the way compute is currently sold makes it awkward.

Whether you rent accelerators directly or submit a batch job, you are typically handed an exclusive set of GPUs and CPUs. That gives you total freedom, and it hands you the entire job of adapting your work to the infrastructure. Worse, billing by card-hour suits a long steady job and punishes a short bursty one, so the tenant overpays for idle capacity and the provider is left holding hardware nobody is using.

It is the difference between renting a whole restaurant kitchen for the evening and needing it for twenty minutes to bake one tray. JoyNexus is the authors' answer, built around treating post-training as a shared service rather than a private allocation.