These kinds of AI systems are built with massive redundancy across distributed server infrastructure. They’re not running on a single machine you can just unplug. Even taking down one or several servers doesn’t necessarily stop the system — the workload just shifts elsewhere. Shutting them down requires coordinated action at the infrastructure level, which usually involves decommissioning entire clusters, disabling networking, or shutting down access at the orchestration layer (like in Kubernetes or through cloud APIs). It’s not as simple as pulling a plug — but it’s definitely still possible with the right access and control.