#opg $OPG

Lately I’ve been less focused on how advanced AI is getting, and more on something that feels more important but less talked about: who actually controls access to it.

Right now,

most AI tools don’t really feel like “owned” technology. They feel more like services you get permission to use.

And that permission can change anytime.

A company update, a policy shift, or even a regional restriction can quietly decide what you can or cannot access.

So in a way, AI is not fully open infrastructure it’s still gated.

That’s why ideas coming from projects like @OpenGradient ($OPG) caught my attention.

Not because they are building “better AI”, but because they are questioning the control layer itself.

Their direction is privacy first and censorship resistant AI.

Using things like TEEs and zkML, the goal is basically to make AI computation happen without exposing your data or giving any single authority full visibility or control over it.

In theory, this pushes AI closer to something like public infrastructure similar to how the internet evolved to bypass control points and centralized restrictions.

But honestly,

it’s not that simple.

Decentralizing AI is not just a feature upgrade.

It’s a full redesign of how computation, trust, and access work. And that comes with trade offs performance issues, technical complexity, trust in hardware systems, and the big one:

whether people will even adopt something less smooth than centralized tools.

So I’m kind of stuck between both sides.

The vision makes sense:

AI as open, permissionless infrastructure. But reality usually adds friction everywhere.

Maybe the real debate is not “centralized vs decentralized AI”, but something deeper will AI become a controlled subscription layer or a shared public utility?

Because whichever direction wins might decide more than just technology…

it might shape how information and intelligence are controlled in the future.

So the question is: do we want AI that we simply use, or AI that no one truly owns?