#opg $OPG
One thing I've realized while watching the AI industry evolve is that openness and concentration seem to be growing at the same time.
Models are becoming more open. Research is shared faster than ever. Yet the infrastructure required to run, verify, and distribute intelligence increasingly sits behind a relatively small number of actors. The technology appears more accessible, while the underlying power structure often becomes less so.
That contradiction is what made me spend time looking at @OpenGradient . Not because decentralization is a guaranteed solution, but because it forces a conversation that feels increasingly important: where does trust actually live in an AI system?
Building open infrastructure is ultimately not just an engineering challenge. It is a governance challenge. Incentives shape behavior, coordination shapes outcomes, and economic power has a habit of concentrating even in systems designed to resist it. Crypto has taught this lesson more than once.
So when I look at decentralized AI networks, I find myself less interested in their technical claims and more interested in their institutional design.
The real question may not be whether intelligence can be made open.
It may be whether open systems can remain trustworthy once they become important enough to matter.
