Where the workflow shifted
On-device models, Private Cloud Compute, app-specific intelligence, and user permission show that AI architecture is now part of UX.
Apps need to explain which work happens on device, which work goes to cloud inference, and which work requires human confirmation.
Tool names are not outcomes
The signal matters when it changes how a team ships, reviews, or recovers work, not when it only names another tool.
Check permissions and failure
- Add one user-facing line per AI feature: what data it uses, where it is processed, whether it leaves the device, and how to turn it off
- Keep the test narrow: one low-risk task or tool entry before connecting permissions, logs, failure handling, and human takeover to production
What still needs proof
If a product only markets intelligence and not processing boundaries, privacy-sensitive users will hesitate. Keep the original source open so the announcement, the evidence, and this site's interpretation stay separate.