Predictive Cost-Benefit Analysis for Generative AI: Estimating the Expected Net Value of Individual Precomputation Tasks Before Execution

The increasing deployment of generative artificial intelligence (AI) systems has created substantial computational expenditure associated with large language model (LLM) inference, proactive response generation, model preparation, KV cache construction, and other predictive computational operations. Recent research on predictive computing has investigated the anticipation of future computational demand and the preparation of reusable computational resources before actual requests arrive. Subsequent developments have extended this paradigm toward multi-layer computational orchestration and economic-aware resource allocation, including GPU workload forecasting, off-peak precomputation, dynamic electricity pricing, and low-cost cloud GPU selection. However, identifying a future computational opportunity or locating a relatively inexpensive execution period does not necessarily establish that a particular precomputation task is economically worthwhile. A task may be associated with a high probability of future demand but still generate insufficient savings to justify its preparation, validation, storage, and maintenance costs. Conversely, a moderately probable task may offer substantial economic value when its potential reuse eliminates expensive future inference operations. This paper introduces Predictive Cost-Benefit Analysis for Generative AI (PCBA-GAI), a conceptual framework that predicts the potential net economic benefit of individual precomputation tasks before their execution. Rather than evaluating predictive computation exclusively through demand probability, aggregate GPU utilization, or computational expenditure, PCBA-GAI estimates the expected financial and computational value generated by each candidate task. Its architecture integrates future request forecasting, expected reuse estimation, avoided inference cost prediction, preparation expenditure estimation, storage and maintenance cost modeling, resource opportunity-cost evaluation, uncertainty-aware benefit estimation, and adaptive task selection. The framework distinguishes between gross computational savings, realized reusable value, direct preparation expenditure, indirect operational costs, and the opportunity cost of consuming resources that could otherwise serve alternative workloads. A task-level net benefit formulation is developed to estimate whether advance computation is expected to produce positive economic value. The formulation further incorporates multiple reuse outcomes, temporal validity, response compatibility, prediction confidence, and the possibility that precomputed resources remain unused. A risk-adjusted decision mechanism is proposed to distinguish tasks with high expected returns from tasks whose estimated benefits are highly uncertain. In addition, the framework introduces marginal net benefit analysis for selecting tasks under constrained GPU, memory, storage, energy, and financial budgets. An experimental methodology is established to compare task-level predictive cost-benefit selection against demand-probability-based selection, frequency-based precomputation, resource-cost-aware scheduling, and conventional reactive inference. The proposed framework extends predictive computing from anticipating future workloads and optimizing execution costs toward explicitly evaluating the economic desirability of each speculative computational investment. Its central research proposition is that predictive computation should be governed not only by whether future demand is likely to occur or whether resources are inexpensive, but also by whether the expected reusable value of an individual task exceeds its complete economic and operational expenditure. The framework does not assume that positive predicted benefits guarantee realized savings; its effectiveness remains subject to empirical validation under realistic workloads, prediction errors, resource constraints, and service-quality requirements.