The debate between local AI and cloud AI is the wrong debate. The real question is which data belongs where, and the answer drives the architecture. Sensitive data, personal records, anything with privacy or compliance implications stays on site on local hardware. General reasoning, heavy compute tasks, and broad analysis go to the cloud where the infrastructure already exists. Two lanes, clear boundary, no philosophical argument required.
This is not a future concept. The tools exist today. A modest local machine running an open source model against a contained and groomed document set handles the private data layer without sending a single record outside the building. The cloud handles the rest. The coordination between the two is the build, and that build requires someone who understands both sides of the pipeline, not just one.
For Virginia’s 131 school divisions the practical path forward is an assessment of what data exists, what needs to stay in house, and what infrastructure is required to make that separation real and defensible. Policy sets the boundary. Architecture enforces it. That work is available now, well ahead of the deployment pressure that is coming. The Hybrid Model is the way.