Cross-Platform Predictive Computing for Generative AI: Forecasting Future AI Request Demand Through Privacy-Preserving Multi-Platform Trend Signals

The increasing adoption of generative artificial intelligence has created substantial computational demand across large language models, image generation systems, video generation platforms, and multimodal AI services. Although existing inference optimization techniques primarily respond to requests after they arrive, emerging information activities across external digital platforms may provide signals about future AI service demand before corresponding requests materialize. However, information demand is distributed across heterogeneous platforms, including search engines, social media, technical communities, news services, video-sharing platforms, and online discussion forums. An individual platform may capture only a partial representation of emerging information interest, limiting the reliability of demand prediction based on isolated information sources. This paper introduces Cross-Platform Predictive Computing for Generative AI (CPCGA), a conceptual framework that investigates whether anonymized and aggregated trend information from multiple digital platforms can be integrated to predict future AI request demand. Unlike conventional single-source trend prediction, CPCGA models cross-platform information activities as complementary and temporally differentiated signals of emerging computational demand. Its architecture integrates multi-platform signal acquisition, privacy-preserving trend representation, cross-platform semantic alignment, temporal signal fusion, future AI request prediction, computational workload estimation, and cost-aware proactive preparation. The framework distinguishes between platform popularity, underlying information interest, and actual AI service demand, recognizing that increased activity on an external platform does not necessarily translate directly into increased AI requests. A probabilistic demand prediction model is formulated to estimate future request volumes, topic distributions, request categories, and prediction horizons using heterogeneous platform-level indicators. Furthermore, a cross-platform reliability mechanism accounts for differences in platform coverage, sampling frequency, demographic composition, information propagation patterns, and temporal relevance. The proposed framework incorporates privacy-preserving design principles by operating on authorized aggregated statistics rather than attempting to identify or reconstruct individual users across platforms. An experimental methodology is established to evaluate the contribution of cross-platform information fusion relative to single-platform prediction, historical-demand forecasting, and conventional trend-aware predictive computing. The research also formulates the computational conditions under which multi-platform forecasting may improve the economic value of proactive inference preparation. This study extends Predictive Computing for Generative AI toward a distributed information-demand prediction paradigm in which heterogeneous digital platforms collectively provide signals for anticipating future computational requirements. Its predictive effectiveness, privacy properties, and practical computational benefits remain subject to empirical validation.