Predictive Semantic Demand Modeling for Generative AI: Anticipating Future User Question Topics and Semantic Categories Before Requests Materialize

The increasing adoption of large language models (LLMs) has created a growing need for computational systems capable of anticipating future user information requirements. Existing generative AI inference optimization techniques primarily respond to requests after they have materialized, while emerging predictive computing approaches investigate the possibility of preparing computational resources in advance. However, predicting future computational demand requires more than estimating request frequency or generating a collection of potentially popular questions. An important intermediate challenge is understanding the semantic structure of future information demand: what topics users are likely to become interested in, what categories of questions they may ask, and how their information requirements may evolve as new events and developments emerge. This paper introduces Predictive Semantic Demand Modeling (PSDM), a conceptual framework for anticipating future user question topics and semantic categories before corresponding requests occur. Rather than predicting only individual future questions, PSDM constructs probabilistic representations of future semantic demand by identifying emerging topics, forecasting their potential development, generating hierarchical semantic categories, and estimating the probability and expected distribution of future question types. Its architecture integrates external information signal acquisition, emerging topic identification, semantic demand classification, temporal semantic forecasting, future question distribution estimation, uncertainty-aware prediction, and computational workload representation. A central contribution of the proposed framework is the distinction between topic prediction, semantic category prediction, and individual question prediction. These represent different levels of abstraction and provide complementary information for anticipating future user demand. The framework further introduces a hierarchical semantic demand representation that connects broad information topics with specific question categories and potential user queries. This representation may enable generative AI infrastructure to prepare computational resources for future information requirements even when the exact wording of future questions is unknown. We formulate mathematical models for predicting future semantic demand, estimating category-level request distributions, evaluating temporal semantic evolution, and determining the expected computational value of predicted semantic workloads. We also establish an experimental methodology for comparing semantic demand prediction with conventional frequency-based forecasting, topic prediction, and direct future question generation. The proposed framework provides a conceptual foundation for extending Predictive Computing for Generative AI from the prediction of individual future computational workloads toward the prediction of structured future information demand. Its predictive accuracy, computational benefits, and practical scalability remain subject to empirical validation.