NeurIPS employed AI detection systems for desk rejections without adequate validation of their accuracy or false positive rates. The systems flagged papers for human review based on unreliable signals, leading to rejections of legitimate submissions. For AI deployment in institutional workflows, this surfaces a specific operational failure: detection systems passed acceptance thresholds despite insufficient calibration testing. High-stakes filtering decisions (rejections that end research careers) require precision metrics equivalent to clinical diagnostics, not general benchmark performance. Academic peer review now faces infrastructure constraints—institutions cannot scale AI-assisted screening without first building internal validation pipelines. For builders, this signals demand for domain-specific validation frameworks before deployment in consequential decisions. For operators, it means rejecting turnkey detection products and instead allocating resources to ground-truth labeling and threshold optimization within institutional contexts. Workflow economics shift: cheaper to validate once than to handle appeal cases and reputational damage. Institutions will likely tier their use of AI detection—routine spam filtering yes, paper rejection no—creating segmented product requirements.