Superspreaders are recognized as being important drivers of disease spread. However, models to date have assumed random occurrence of superspreaders, irrespective of whom they were infected by. Evidence suggests though that those individuals infected by superspreaders may be more likely to become superspreaders themselves. Here, we begin to explore, theoretically, the effects of such a positive feedback loop on (1) the final epidemic size, (2) the herd immunity threshold, (3) the basic reproduction number, R₀, and (4) the peak prevalence of superspreaders, using a generic model for a hypothetical acute viral infection and illustrative parameter values. We show that positive feedback loops can have a profound effect on our chosen epidemic outcomes, even when the transmission advantage of superspreaders is moderate, and despite peak prevalence of superspreaders remaining low. We argue that positive superspreader feedback loops in different infectious diseases, including SARS-CoV-2, should be investigated further, both theoretically and empirically.