Predictive Computational Value Assessment for Generative AI: Measuring the Net Benefits of Proactive Computation Against Reactive Inference

The increasing adoption of generative artificial intelligence (AI) has created growing interest in predictive computing, in which computational workloads are anticipated and selectively executed before corresponding user requests materialize. Existing research on predictive inference preparation, predictive computational orchestration, and economic-aware predictive computing has investigated how future computational demand can be forecast, how reusable computational resources can be prepared, and how advance computation can be scheduled under varying infrastructure and economic conditions. However, an important question remains insufficiently addressed: how can the actual value created by predictive computation be rigorously measured relative to conventional reactive inference? Accurate demand prediction, successful advance execution, reduced online inference expenditure, and improved resource utilization do not independently establish that predictive computation provides an overall benefit. Advance computation introduces additional expenditure associated with forecasting, candidate workload generation, speculative execution, validation, storage, maintenance, resource occupation, and potentially unused computational results. This paper introduces Predictive Computational Value Assessment (PCVA), a conceptual evaluation framework for quantifying the net computational, economic, operational, and service-level benefits of predictive computation in generative AI systems. The proposed framework establishes a counterfactual comparison between predictive and reactive execution under equivalent workload, response-quality, and service-level requirements. Its architecture integrates six evaluation dimensions: (1) avoided online computational expenditure, (2) predictive preparation expenditure, (3) resource opportunity cost, (4) realized reuse and prediction failure, (5) operational and economic externalities, and (6) response quality and service-level preservation. A unified mathematical formulation is developed to estimate workload-level expected net value and system-level realized net benefit while distinguishing gross computational savings from net computational savings and financial savings. The framework further introduces prediction-adjusted value, marginal preparation value, benefit realization efficiency, and net value per successfully served request as complementary evaluation measures. Particular attention is given to counterfactual cost estimation, resource attribution, double-counting prevention, and the distinction between theoretical expected benefits and experimentally observed outcomes. An experimental methodology is proposed to compare reactive inference, conventional caching, predictive preparation, and economic-aware predictive orchestration under matched workload and service constraints. The proposed research does not assume that predictive computation is universally beneficial. Instead, it establishes a systematic methodology for determining when advance computation creates measurable net value, when its benefits are negligible, and when speculative computation should be avoided. By treating realized net benefit as the ultimate evaluation criterion, PCVA provides an independent decision-theoretic foundation for assessing the practical effectiveness and economic viability of predictive computing in generative AI infrastructures.