Edge AI vs cloud: where should compute live?

	Edge AI	Compute Locator
Definition	Intelligence that runs on the device itself rather than in the cloud — one of the layers that turn static hardware into systems that keep evolving after sale.	A companion tool that matches each device workload to edge, hub, or cloud by weighing latency, bandwidth, cost, and battery life. Companion tool: Compute Locator

The difference

Edge AI runs on the device: fast, private, and working when the network is not, at the cost of silicon, power and a model that cannot be swapped overnight. Cloud compute is the reverse: unlimited capacity and instant model updates, bought with latency, bandwidth cost and a product that stops working when the service does. Humane's AI Pin and Rabbit's R1 both chose cloud-only with no fallback, and both became e-waste when the service went. The book's Compute Locator makes the call workload by workload.

Try it on your product

Compute Locator: edge, hub or cloud, per workload.

From the chapter of Tangibles that introduces these terms. Definitions are the book's glossary entries.

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