A commentary argues that pharma’s AI bottleneck in real-world data analysis is less about model size and more about the semantic layer used to interpret clinical meaning. The piece highlights that precise terminology—where clinical concepts live—limits reasoning quality even when computation and training data are scaled. The point matters for biotech teams implementing AI for evidence generation, label expansion, and trial optimization, because performance can plateau when ontology mapping, coding consistency, and clinical interpretation are not handled with the same rigor as model architecture.