Predictive Break-Even Analysis for Generative AI: Determining the Accuracy Thresholds at Which Proactive Computation Becomes Economically Beneficial

Predictive computing offers a potential mechanism for improving the computational efficiency of generative artificial intelligence (AI) systems by anticipating future inference demand and preparing computational resources before corresponding requests arrive. However, the economic value of predictive computation depends critically on whether future demand can be predicted with sufficient reliability to justify the expenditure associated with advance preparation. A predictive system may generate substantial computational waste when anticipated workloads fail to materialize, while an insufficiently accurate demand model may cause unnecessary GPU consumption, storage utilization, validation expenditure, and resource competition. Existing predictive computing approaches have established architectural and optimization principles for proactive inference preparation, infrastructure orchestration, and economic-aware resource allocation. Nevertheless, an important decision-theoretic question remains insufficiently formalized: how accurate must a prediction be before advance computation becomes economically worthwhile? This paper introduces Predictive Break-Even Analysis for Generative AI (PBEA-GAI), a conceptual framework for determining the minimum predictive reliability required to justify proactive computational expenditure. The framework investigates the relationship between prediction accuracy, future demand probability, successful resource reuse, preparation expenditure, avoided online inference costs, resource opportunity costs, and economic uncertainty. Rather than treating prediction accuracy as an independent performance metric, PBEA-GAI incorporates prediction reliability into an explicit economic decision model. A fundamental break-even condition is derived to determine the minimum probability of future demand required for a candidate workload to produce nonnegative expected net computational savings. The analysis is further extended to account for heterogeneous prediction errors, asymmetric false-positive and false-negative costs, partial resource reuse, dynamic electricity pricing, cloud GPU expenditure, storage maintenance, and multiple prediction horizons. The framework distinguishes conventional classification accuracy from precision, recall, probability calibration, and cost-weighted prediction performance. It demonstrates theoretically why a universal accuracy threshold cannot adequately determine the economic feasibility of predictive computation. Instead, the required prediction threshold depends on the relative magnitude of expected computational savings and total preparation expenditure. A cost-sensitive selection mechanism is proposed to determine which predicted workloads should be prepared, deferred, or rejected. Furthermore, an experimental methodology is established to evaluate break-even prediction thresholds under different workload distributions, hardware configurations, economic conditions, and resource availability constraints. The proposed research provides a decision-theoretic foundation for determining when predictive computation should be activated, how much predictive confidence is economically necessary, and how computational preparation decisions should change when resource costs or future demand conditions fluctuate. Its practical effectiveness and the validity of its theoretical assumptions remain subject to empirical investigation.