Lifecycle-Aware Predictive Computing for Generative AI: Forecasting the Emergence, Growth, Peak, and Decline of Future Information Demand

The computational demand of generative artificial intelligence systems is strongly influenced by the temporal evolution of information popularity. Emerging technologies, breaking news, scientific discoveries, public events, and rapidly developing discussions can generate substantial fluctuations in user queries. Importantly, the popularity of an information topic does not remain constant over time. It typically evolves through multiple stages, including initial emergence, acceleration, growth, peak activity, and subsequent decline, sometimes followed by renewed attention. Conventional inference systems generally respond to requests after they arrive, while existing caching and predictive preparation mechanisms often emphasize historical request frequency, short-term popularity estimation, or the prediction of future demand without explicitly modeling its complete lifecycle. This paper introduces Lifecycle-Aware Predictive Computing for Generative AI (LAPCGA), a conceptual framework that integrates information-demand lifecycle forecasting with proactive computational preparation. Rather than predicting only whether a particular question will become popular or estimating its future request volume at a single point in time, LAPCGA investigates the prediction of the temporal evolution of information demand. Its objective is to estimate when a topic or question is likely to emerge, how rapidly its popularity will increase, when it may reach its maximum demand, how long elevated demand may persist, and when its popularity is expected to decline. The proposed framework incorporates lifecycle stage identification, multi-horizon demand forecasting, topic-to-question expansion, popularity acceleration estimation, peak-time prediction, demand-decay modeling, and lifecycle-dependent computational scheduling. A central component is a lifecycle-aware optimization formulation that evaluates the expected computational value of preparing responses at different stages of predicted demand evolution. This enables the system to distinguish between early-stage preparation, growth-stage resource expansion, peak-stage computational protection, and decline-stage preparation reduction or expiration. The framework also considers prediction uncertainty, response freshness, preparation lead time, reusable computational resources, and the possibility of renewed demand. We establish a mathematical formulation for lifecycle-aware predictive computation and propose an experimental methodology for evaluating its effectiveness against reactive inference, conventional caching, historical-frequency prediction, and demand forecasting without lifecycle modeling. The principal hypothesis is that explicitly forecasting the lifecycle of future information demand may improve the timing and economic efficiency of proactive inference preparation. The proposed framework does not claim experimentally established improvements; its practical effectiveness, generalizability, and computational advantages remain subject to empirical validation.