A report warns that AI cannot overcome oncology drug discovery failures when preclinical models do not reflect patient tumor biology. The piece argues that despite growing use of sequencing, high-throughput screening, and machine learning across discovery stages, candidate failure rates remain high because model training data may lack clinical relevance. It contrasts patient-derived data—which are biologically relevant but limited in scale—with model systems such as cell lines, organoids, and patient-derived xenografts, which provide scalability but can miss critical patient context including treatment history-driven resistance. For AI-focused biotech teams, the message is operational: the bottleneck may be data representativeness and functional biology fidelity rather than algorithmic sophistication.