Something I keep returning to is the assumption that AI models compete on capability. OpenGradient's Model Hub makes me question whether that's actually the right frame.
When you can publish a model on-chain with verifiable inference history attached, the competition shifts. It's no longer purely about benchmark scores. It becomes about track record. About whether a model's outputs can be audited across deployments, not just evaluated in isolation before someone commits to using it.
Most model marketplaces today are discovery tools. Browse, download, deploy. The history of how a model performed elsewhere doesn't travel with it.
What I find interesting about the Model Hub architecture is the implication that provenance could become a competitive signal. Not just what a model can do, but what it demonstrably did, verified, on-chain, across prior use.
That changes the selection dynamic quietly. Developers stop choosing models purely on specs. They start choosing on documented reliability.
Whether that actually drives adoption or remains a feature most builders ignore is the part I genuinely can't call yet.