A 13-institution position paper (arXiv 2505.18893) argues that AI benchmark evaluations, the de facto standard for assessing model capability, are structurally limited to first-order effects (immediate system outputs) and cannot measure second-order effects such as workforce transformation, behavioral shifts, or downstream societal impacts in deployment. The authors identify specific failure modes including benchmark saturation, leaderboard overfitting, data leakage, static single-turn design, and inability to capture human-AI interaction dynamics in context. The paper proposes a new evaluation ecosystem combining field testing, red teaming, and stakeholder engagement grounded in value-sensitive design methodology. For financial institutions, this is structurally relevant because it challenges the validity of benchmark-based claims used to justify AI procurement, investment theses, and regulatory compliance assertions, the gap between benchmark performance and real-world ROI is a live question on enterprise AI adoption.
315 other tracked developments touching the same claims.