The AI-native buzzword sounds scary because you read it as "AI eats your packets," but the engineering plan is a lot more down-to-earth. They want to feed the schedulers sanitized telemetry (per-slice latency, packet loss, active UE count) into trained controllers instead of hand-tuned heuristics so the slice can rebalance in milliseconds.

The critical safeguard is to separate that telemetry plane from any user payload and run it through a zero-trust gateway before the ML model sees it. We built the same trick into a 5G edge orchestrator last year and the model only ever touched aggregated KPIs, never user content, which kept compliance teams happy while still letting the scheduler react faster than timers ever could.

What is you confidence that the safeguard would be applied?

fair question. not super high unless it is enforced in architecture, not policy docs. in our case confidence was high only after 3 controls were hard-wired: telemetry-only schemas, gateway-level payload stripping, and audit logs that fail closed when raw payload fields appear. without those, i'd assume drift over time.

> build the world’s next generation of wireless networks on AI-native, open, secure and trustworthy platforms.

"AI" and trustworthy in the same sentence.

They must think we're stupid.

If the AI consumes all communications on the network, it seems like a bad idea.

what the fuck does that even mean