Edge AI
Definition
Running AI models directly on devices at the edge of the network — smartphones, IoT devices, industrial sensors, vehicles — instead of in central data centres or cloud APIs. Typically uses quantised or distilled models to address hardware constraints on memory, compute and energy.
Noise — Signal
Edge AI is often sold as "a privacy solution because nothing goes to the cloud". The privacy advantages are real but limited: the model itself comes from a cloud training environment, updates require telemetry, and the threat model shifts — device theft and reverse engineering become more relevant than API eavesdropping. Edge hardware is also the limiting factor: what runs on a smartphone in 2026 is significantly smaller than a typical frontier model, with correspondingly limited quality in language understanding, reasoning and multimodality.
The right question
Not: "Can we push this to the edge?" But: "Which concrete requirements — latency, offline capability, privacy, bandwidth cost, regulatory locality — justify the edge stack, and which quality trade-offs do we accept against a cloud model for exactly those requirements?"