The second approach offers broader feature support, seen in projects like Cloud Hypervisor or QEMU microvm. Built for heavier and more dynamic workloads, it supports hot-plugging memory and CPUs, which is useful for dynamic build runners that need to scale up during compilation. It also supports GPU passthrough, which is essential for AI workloads, while still maintaining the fast boot times of a microVM.
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But the note cautioned against treating AI as an “immediate fiscal windfall.” Before productivity fully kicks in, governments face upfront costs that could strain budgets already burdened by post-pandemic debt. Government spending explicitly earmarked for AI remains modest—often only a fraction of a percent of GDP—but a sea of hidden costs could make the transition much more difficult for budgets to handle.