Choosing Without a Winner: Bayesian Frontier Inference for Lifecycle Decisions

As AI systems are evaluated across an expanding range of dimensions, the prac- tical question of which system to use has become increasingly difficult to answer from benchmark scores alone, since those scores compress uncertainty, obscure tradeoffs and often fail to distinguish changes in the systems themselves from changes in the regimes through which they are measured. We develop a Bayesian framework for learning multidimensional AI system frontiers that treats each eval- uated configuration as a composite of model, harness and version, inference effort and other execution settings, jointly representing performance, cost, latency, task heterogeneity and measurement uncertainty while preserving the structure of evi- dence that would otherwise disappear into a scalar ranking. Separating changes in the available set of systems from shifts in task populations, evaluators and opera- tional environments allows the temporal model to describe how the frontier itself evolves, which in turn makes it possible to connect that evolving frontier to organi- zation specific losses, constraints, switching costs, fallback options and planning horizons when deciding whether a system should be adopted, retained, replaced or retired. Extending this decision framework with value of information analysis identifies which additional evaluations are most consequential, yielding a unified basis for choosing among AI systems and revisiting those choices as capabilities, circumstances and the evidence available to judge them continue to change