Predictive Demand Drift Detection for Generative AI: Anticipating Changes in User Demand Patterns to Adapt Future Computational Preparation

The increasing deployment of generative artificial intelligence (AI) systems has created computational environments characterized by continuously changing user interests, evolving information requirements, emerging topics, and nonstationary inference workloads. Existing predictive computing approaches investigate how future computational demand can be anticipated and how computational resources can be prepared before corresponding requests materialize. However, predictive preparation introduces an important challenge: the patterns underlying future demand may change over time. A predictive model trained on historical requests may gradually become inaccurate when users shift their interests, new information events emerge, existing topics lose relevance, or the computational characteristics of requests evolve. Consequently, previously selected precomputation tasks, prepared responses, resource reservations, and computational schedules may become inappropriate. This paper introduces Predictive Demand Drift Detection for Generative AI (PDDD-GAI), a conceptual framework that investigates the early identification of changes in user demand patterns and the translation of detected changes into adaptive computational decisions. Unlike conventional demand forecasting, which estimates future request volumes and computational requirements, PDDD-GAI focuses on identifying whether the statistical, semantic, temporal, and computational characteristics of demand are changing, determining the magnitude and direction of such changes, and estimating their potential implications for future inference workloads. The proposed architecture integrates multidimensional demand representation, semantic distribution monitoring, temporal change-point detection, prediction residual analysis, external information signal monitoring, drift severity estimation, adaptive forecasting, and predictive computational strategy revision. It distinguishes between sudden demand drift, gradual demand drift, recurring drift, emerging-topic drift, semantic drift, and computational-profile drift. A unified mathematical formulation is developed to characterize demand distribution changes and estimate their expected computational consequences. The framework further introduces a drift-aware preparation mechanism that dynamically updates anticipated questions, response expiration policies, reusable computational resources, GPU reservations, and economic scheduling decisions according to detected changes. An experimental methodology is established to evaluate detection accuracy, detection delay, false alarm rates, forecasting recovery, computational waste, and total system expenditure under changing demand environments. Particular attention is given to the distinction between detecting historical distribution changes and predicting future demand changes before their full effects become observable. The proposed framework does not assume that every detected distribution change represents meaningful user demand drift; rather, it investigates how multiple information sources and computational indicators can be combined to identify operationally significant changes. Its practical effectiveness remains subject to empirical validation.