Can a model trained only on birdsong classify elephant calls — without any fine-tuning at all? A new paper from Geldenhuys and Niesler runs frozen-embedding transfer from bird-trained and speech-trained foundation models to African and Asian elephant calls, and gets within 2.2 percent of an end-to-end supervised baseline. Even more striking: the second layer of the network outperforms the final layer, and ten percent of the parameters do most of the work. Cross-domain parallel: the convergent evolution of vocal learning across songbirds, parrots, cetaceans, and elephants — different anatomies, different ecologies, similar architectural primitives.