Computational Wind Engineering is entering a new phase shaped by GPU-accelerated CFD, physics-based learning, and agentic workflow automation. This keynote examines how accelerated computing can expand the scale and cadence of simulation campaigns, enabling more wind directions, inflow conditions, geometry variants, uncertainty studies, and validation cases. It also discusses how learned models and automated workflows can reduce time-to-answer without replacing CFD, wind-tunnel testing, field measurements, or engineering judgment. The focus is practical: faster solvers, more repeatable workflows, stronger links to evidence, and simulation campaigns that better support design decisions.