Advances in AI have shown promise in protein structural information, but in this perspective, the authors argue that biology’s true complexity—emergent, evolved, and irreducibly messy—demands more than bigger models or more data and that unlocking a genuine “biological” AI requires anchoring machine learning in canonical, process-based “world models,” rather than chasing a universal theory built from parts alone.