Prediction Error Cost Modeling for Generative AI: Quantifying the Economic and Computational Losses of Unsuccessful Predictive Precomputation

Predictive computing provides a potential mechanism for improving the computational efficiency of generative artificial intelligence (AI) by anticipating future inference demand and preparing reusable computational resources before actual requests arrive. However, predictive computation introduces an important source of operational expenditure that is not adequately characterized by prediction accuracy alone: the cost of incorrect predictions. When an anticipated request does not materialize, the computational resources allocated to its advance preparation may become partially or entirely wasted. Similarly, when actual demand is not predicted, the system may lose opportunities to avoid repeated inference expenditure, reduce peak computational pressure, or exploit favorable resource availability. These two forms of prediction error generate fundamentally different economic consequences and require explicit cost modeling. This paper introduces Prediction Error Cost Modeling for Generative AI (PECM-GAI), a conceptual framework for quantifying, predicting, and controlling the computational and economic losses associated with inaccurate predictive computation. The proposed framework distinguishes between false-positive precomputation, in which prepared resources remain unused, and false-negative prediction, in which actual computational demand is not anticipated. It further considers partial resource reuse, response incompatibility, information expiration, excessive preparation, resource displacement, cloud execution expenditure, electricity consumption, storage overhead, validation costs, and opportunity costs. A prediction error cost model is developed to translate classification errors, request-count forecasting errors, timing errors, and computational compatibility errors into measurable economic consequences. The framework introduces workload-specific loss functions, asymmetric error penalties, expected prediction loss, uncertainty-adjusted preparation decisions, and prediction regret. It also establishes a decision-theoretic optimization formulation that jointly considers the probability of future demand, the expected number of requests, the probability of successful resource reuse, the cost of advance preparation, and the potential losses associated with incorrect decisions. Importantly, PECM-GAI extends conventional predictive computing evaluation beyond forecasting accuracy and total computational expenditure by explicitly examining the financial consequences of prediction errors. It investigates whether a predictive computing system can improve its decisions by estimating the cost of being wrong before committing computational resources. An experimental methodology is proposed to compare accuracy-oriented, cost-aware, and uncertainty-aware prediction strategies under identical workloads and resource constraints. The framework does not assume that prediction errors can be eliminated or that every prediction can be economically optimized. Rather, it establishes a theoretical foundation for determining which prediction errors are most expensive, how their consequences can be quantified, and when avoiding an incorrect precomputation decision is more valuable than increasing the number of predicted workloads.