I found myself rereading a note on decentralized AI late at night, not because it was new, but because something in it still felt unresolved.
It first feels like another infrastructure wave, but the framing shifts: AI today behaves less like ownership and more like permission. Access can be tuned, limited, or revoked by whoever controls the interface layer.
From that view, @OpenGradient , OPG ($OPG) feel less like model projects and more like control-surface experiments. Privacy-first generation, TEEs, zkML attempts to make computation less visible to operators and observers.
The tension is not in the tools, but in the incentive stack beneath them. The systems that scaled AI were not designed for invisibility. Removing visibility without adding new gatekeepers feels like coordination, not engineering.
Maybe I’m overstating it… still early.
What stays is the human layer: builders reducing exposure, users asking for less permissioned access, systems renegotiating trust without full visibility.
And I keep wondering if value routes through invisible execution paths, who defines “open”?