The interesting question is not whether the cloud is fast. Of course it can be fast. The interesting question is whether the job deserves to leave the machine sitting in front of the person.
The local-first compute thesis treats placement as a human decision as well as a scheduler decision: stay local when latency or privacy makes that right; propose a temporary burst only when the task earns it. It is a design target, not a shipped control plane in this repository.
Memory pressure, watts, and queue time are measurable. A person’s reason for keeping work local is also real, even when it does not fit comfortably in a scheduler objective.
A good private agent should be able to say: this will take a while here; it could finish faster there; here is what would move; here is the cost; do you want that? That is a better interface than silent migration.
A simulator gives us a way to argue about policy without spending money, collecting workload data, or pretending a reference diagram is already a control plane.
Build a placement simulator first. Compare resource-only policy, privacy-first policy, and owner-overridable policy on the same trace before provisioning any cloud resource.
Read the source-backed research note before treating this essay as a product promise.
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