What AI tools have changed most sharply in research practice is not the pace of hypothesis generation — it is who bears the cost of quality control. When an AI produces a protein sequence or a literature citation, the scientist cannot assume plausibility is correctness. The empirical evidence from AI-derived drug patents shows scientific progress is real — but the institutional infrastructure for validating that progress has not kept pace with the volume of outputs demanding validation. Journals and labs that built their workflows around human-authored claims are now auditing…