Personalized Predictive Computing for Generative AI: Anticipating Individual Users' Future Information Needs Before Requests Materialize

The increasing integration of large language models (LLMs) into personalized digital environments has created an opportunity to reconsider how computational demand is anticipated and managed. Conventional generative AI systems primarily allocate computational resources after users submit requests, while emerging predictive computing approaches investigate whether future information demand can be identified and computationally prepared in advance. However, predicting aggregate demand does not necessarily reveal what a particular individual will need in the future. Different users may encounter the same external event but require substantially different explanations, analyses, recommendations, and computational tasks according to their individual interests, ongoing activities, knowledge requirements, and interaction histories. This paper introduces Personalized Predictive Computing for Generative AI (PPC-GAI), a conceptual framework that investigates the prediction of individual users' future information requirements and the proactive preparation of corresponding computational resources. Rather than relying exclusively on public information trends or aggregate request distributions, PPC-GAI incorporates authorized user interaction histories, evolving interest representations, ongoing task contexts, temporal behavioral patterns, and external information signals to estimate the probability and expected computational value of future user-specific requests. The framework integrates personalized demand modeling, future information-need inference, candidate query generation, contextual compatibility assessment, expected-value estimation, proactive response preparation, computational-state reuse, and privacy-aware resource scheduling. A central feature of the proposed architecture is the distinction between predicting what a user may need and determining whether that predicted need justifies advance computation. Candidate information requirements are evaluated according to their predicted occurrence probability, expected recurrence, response-generation cost, contextual stability, temporal validity, reuse potential, and preparation expenditure. The framework further introduces a hierarchical demand representation that distinguishes individual-specific requirements, cross-user recurring requirements, and globally emerging information needs. This representation provides a conceptual basis for coordinating personalized predictive computation with broader predictive computing architectures. The paper develops a mathematical formulation for personalized future-demand prediction and cost-aware computational preparation, discusses privacy-preserving approaches to user modeling, and establishes an experimental methodology for evaluating prediction accuracy, computational savings, personalization quality, and response reliability. The proposed framework does not assume that individual future needs can be predicted perfectly or that proactive preparation is universally beneficial. Instead, it investigates the conditions under which personalized demand prediction may enable generative AI systems to prepare relevant information and computational resources before individual requests materialize.