Model collapse, the degradation in performance that arises when generative models are trained on the outputs of prior models, has largely been studied in the abstract. This work argues that model collapse is not just a theoretical curiosity but a threat to low-resource and marginalised communities. By reducing training efficiency and skewing data distributions away from the tails of their support, model collapse disproportionately impacts these communities, both environmentally and culturally. We examine these implications and propose strategies to mitigate the disparate impact of model collapse on low-resource communities.