A commentary piece directly challenges the idea that AI can compensate for preclinical models that don’t reflect patient tumor biology. The analysis argues that higher data volumes and better analytics don’t fix a core mismatch: training data that fail to generalize to real patient response. The piece emphasizes the tension between scalable model systems (cell lines, organoids, patient-derived xenografts) and higher-relevance patient-derived data that often lack breadth. It also highlights how treatment history can shape resistance—an element frequently missing from standard preclinical setups. The message for biotech leaders is clear: AI adoption must be paired with improved biological fidelity across the discovery pipeline, not treated as a substitute for it.