One thing I've noticed lately is that the AI conversation keeps focusing on models, while the real power often sits in the infrastructure that hosts, serves, and verifies them.
That's partly why @OpenGradient caught my attention. The idea isn't just decentralized AI compute; it's the harder question of how trust gets distributed when intelligence itself becomes network infrastructure. Hosting models openly is one challenge. Coordinating incentives, verification, and governance across participants is another entirely.
What I find interesting is the tension. Crypto has spent years trying to reduce reliance on centralized intermediaries, yet AI development seems to be concentrating power around a small number of organizations with access to data, compute, and distribution. Projects like OpenGradient are exploring alternatives, but technical openness alone doesn't solve institutional concentration.
A decentralized network can still end up governed by a small set of actors if incentives, validation rights, or infrastructure ownership become concentrated. The social layer matters as much as the technical one. #OPG
I'm watching this space with cautious interest. The real test may not be whether decentralized AI works, but whether people can coordinate around it in a way that creates lasting trust.