"Censorship-resistant" is one of those phrases I've learned to interrogate immediately. Resistant to what, exactly, and resistant compared to what baseline.
OpenGradient's pitch is that machine learning models run on decentralized infrastructure rather than a single company's servers, meaning no central party can quietly alter outputs or pull access. That's a real structural difference from how most AI products operate today, where one company controls the model and the API key.
What I'd want to see is how inference actually gets distributed across nodes. Decentralized infrastructure still needs someone running those nodes, and if a small set of operators controls most of the compute, the resistance is more theoretical than functional.
The architecture points the right direction. Who actually runs it still decides whether the claim holds.
#opg $OPG @OpenGradient
OpenGradient's pitch is that machine learning models run on decentralized infrastructure rather than a single company's servers, meaning no central party can quietly alter outputs or pull access. That's a real structural difference from how most AI products operate today, where one company controls the model and the API key.
What I'd want to see is how inference actually gets distributed across nodes. Decentralized infrastructure still needs someone running those nodes, and if a small set of operators controls most of the compute, the resistance is more theoretical than functional.
The architecture points the right direction. Who actually runs it still decides whether the claim holds.
#opg $OPG @OpenGradient