Amid intensifying drought conditions and surging industrial water demands, researchers at Virginia Tech have developed a national-scale causal artificial intelligence model designed to resolve competing water needs between agriculture and semiconductor manufacturing. Published in the Journal of Water Resources Planning and Management, the study introduces a data-driven framework to guide policymakers through complex, cross-jurisdictional water allocation challenges. Led by associate professor Feras Batarseh, alongside A3 lab members Ph.D. candidate Lauren Pincus and research associate Dan Sobien, the team engineered a model that moves beyond traditional predictive analytics. Instead of forecasting outcomes in isolation, the causal AI identifies direct cause-and-effect relationships across hydrological, agricultural, climatic, and industrial datasets. By analyzing semiconductor facility footprints, regional irrigation patterns, and water stress indicators across all fifty states, the system maps how industrial expansion in one basin directly impacts agricultural capacity in adjacent regions. The model addresses a critical national tension: semiconductor fabrication requires vast quantities of ultra-purified water for silicon wafer processing, with major facilities concentrated in water-stressed states such as Arizona, California, and Texas. Simultaneously, agriculture consumes approximately seventy percent of U.S. freshwater withdrawals, relying heavily on irrigation for staple crops like corn, cotton, and soybeans. In shared watersheds like the Colorado River, increased chip production can directly erode irrigation reliability. Conversely, the AI demonstrates that optimizing agricultural water efficiency can simultaneously sustain crop yields and free up capacity for industrial growth, effectively decoupling economic expansion from resource depletion. Designed for multi-tier implementation, the model generates state-specific optimization strategies while accounting for the interdependence of municipal supplies, federal jurisdictions, and economic zones. Batarseh emphasized that aging infrastructure, cybersecurity vulnerabilities, and climate volatility further complicate water security, making integrated decision-making essential. The AI platform enables scenario simulation, policy stress-testing, and targeted efficiency recommendations, allowing state water managers and federal agencies to preempt shortages rather than react to crises. By treating water as a dynamic, interconnected network rather than isolated municipal resources, the research provides a scalable pathway to balance domestic manufacturing priorities with long-term hydrological sustainability. The model marks a significant step toward evidence-based resource governance, positioning artificial intelligence as both a diagnostic tool and a strategic framework for national water security.