Event-Driven Burst Demand Prediction for Generative AI: Forecasting AI Request Surges Triggered by Emerging News and Trending Events

The increasing integration of generative artificial intelligence into everyday information services has created a new computational challenge: sudden surges in AI inference demand triggered by emerging news, major public events, technological announcements, financial developments, and rapidly spreading social trends. Unlike conventional workload fluctuations, event-driven demand bursts may emerge within short periods, increase rapidly, reach substantial peaks, and subsequently decline as public attention shifts. These events can generate large volumes of semantically related but individually distinct user requests, placing substantial pressure on GPU capacity, inference latency, memory availability, and computational expenditure. Although existing generative AI infrastructure employs workload forecasting, reactive autoscaling, semantic caching, and inference optimization, predicting general resource consumption is not equivalent to anticipating the specific information-related computational demand generated by an emerging event. This paper introduces Event-Driven Burst Demand Prediction (EBDP), a predictive framework designed to anticipate sudden increases in generative AI requests before their full computational impact materializes. EBDP integrates emerging event detection, cross-platform information signal analysis, event-to-query demand inference, burst magnitude forecasting, temporal demand evolution prediction, computational workload estimation, and proactive resource preparation. Its central objective is to transform early signals of external events into probabilistic forecasts of future AI request volume and associated computational requirements. The framework distinguishes between event occurrence prediction, request burst prediction, peak demand estimation, and burst duration forecasting, recognizing that these represent related but fundamentally different prediction tasks. A mathematical formulation is developed to estimate event-conditioned request intensity, expected computational expenditure, and the net value of proactive preparation. The framework further introduces event-aware scheduling mechanisms that consider prediction confidence, forecast lead time, GPU availability, information freshness, and the risk of unused precomputed resources. An experimental methodology is established to evaluate prediction accuracy, burst anticipation, computational savings, response quality, and service reliability against reactive and conventional predictive baselines. This research positions event-driven burst forecasting as a specialized extension of Predictive Computing for Generative AI, investigating how generative AI infrastructure may anticipate computational demand caused by emerging real-world events rather than waiting for the resulting request surge to occur. The proposed framework is conceptual, and its practical effectiveness remains subject to empirical validation.