The cloud is convenient. But more and more people are beginning to feel a quiet discomfort with entrusting everything to it.
Information is stored, utilized, linked, and predicted. Our behaviors, emotions, preferences, and relationships are being processed in places we can’t see. This unease is no longer limited to a tech-savvy minority.
So what can we do to protect privacy in a world like this?
One possible answer, I believe, is to bring AI down from the cloud.
As we can see in Apple’s recent strategic direction, AI is shifting from the cloud to the device itself. Inside the iPhone, inside the Mac, AI knows you, processes your data, and crucially—doesn’t send it out.
When computing power resides locally and your data stays in your hands, that convergence creates a new kind of architecture—one where security isn’t just about convenience, but trust. This is how a “safer-than-cloud” AI environment can emerge.
In this context, the question of “Where does AI run?” becomes more than just a technical choice. It evolves into a political and ethical question: “Who holds your data?” and equally important, “Who does not see it?”
This shift opens the door to new architectural possibilities.
When individuals hold their own data and run their own models locally, we create a form of AI that operates with a completely different risk structure and trust model than large-scale cloud systems.
In an era where the cloud itself is starting to feel “uncomfortable,” the real question becomes: Where is computation happening, and who is it working for?
Protecting privacy doesn’t mean restricting AI from using personal information.
It means enabling usage—without giving it away. That’s a design problem.
AI and privacy can coexist.
But that coexistence will not be found in the cloud.
It will be realized through a rediscovery of the local—through the edge.