A technical piece on AI drug repurposing argued that performance bottlenecks often come from fragmented or inconsistent foundational data rather than insufficient model scale. The article emphasized that many AI agents can identify patterns but may struggle to validate data quality, study design, and causality when inputs aren’t standardized. The authors suggest improving structured inputs using normalized, expert-curated knowledge graphs to map relationships across genes, variants, pathways, diseases, and existing biologics. The proposal targets more reliable extraction of repurposing hypotheses before moving to downstream experimental validation. The message is a reminder that translational success in AI repurposing depends on data governance and verification workflows as much as on algorithm design.