Explainable Predictive Computing for Generative AI: Interpreting Why Computational Tasks Are Prepared Before Demand Materializes

The increasing deployment of generative artificial intelligence (AI) systems has introduced increasingly complex computational management decisions involving future demand prediction, proactive inference preparation, GPU resource allocation, task scheduling, energy consumption, and economic optimization. Predictive computing provides a potential mechanism for anticipating future computational requirements and preparing selected resources before corresponding user requests arrive. However, as predictive computing systems become more autonomous, an important question remains insufficiently addressed: why does a system decide to prepare a particular computational task in advance rather than wait for actual demand to materialize? Existing predictive computing approaches primarily investigate demand forecasting, computational resource preparation, and optimization objectives, but the reasoning connecting predicted demand to individual computational decisions may remain difficult for operators and users to understand. This paper introduces Explainable Predictive Computing for Generative AI (EPC-GAI), a conceptual framework that integrates decision-level explainability into predictive computational infrastructures. The proposed framework investigates how systems can explain why specific computational tasks are selected, when advance computation should occur, which resources should be allocated, and under what conditions previously selected tasks should be deferred or canceled. Its architecture integrates six explainability dimensions: predictive signal attribution, demand prediction explanation, computational benefit explanation, resource allocation explanation, risk and uncertainty explanation, and counterfactual decision explanation. Rather than presenting prediction confidence or optimization scores as sufficient explanations, EPC-GAI reconstructs the complete decision pathway connecting available information signals, predicted future workloads, estimated computational benefits, resource constraints, and final execution actions. A mathematical formulation is developed to characterize task-level decision attribution and counterfactual explanations. The framework distinguishes between explanations of predictive outcomes and explanations of optimization decisions, recognizing that a task may receive a high demand prediction score but still be rejected because its preparation cost, resource requirements, or expiration risk exceeds its expected benefit. Particular attention is given to multi-objective decision-making involving computational cost, latency, energy consumption, service quality, and resource availability. The paper also introduces a structured explanation record that connects each predictive decision to its supporting signals, model outputs, optimization constraints, execution history, and subsequent realized outcomes. An experimental methodology is proposed to evaluate explanation fidelity, completeness, stability, usefulness, computational overhead, and consistency with actual system behavior. The framework is designed to complement existing predictive computing, predictive computational orchestration, and economic-aware predictive computing architectures rather than replace their underlying optimization mechanisms. Its practical effectiveness and the reliability of its explanations remain subject to empirical validation.