#opg $OPG I’ve been around crypto long enough to know that most of the noise comes from people solving the wrong problem. A lot of “verifiable AI” projects keep asking the same thing: how do you prove inference ran correctly? OpenGradient does address that. But what stands out to me is that they also go after something a lot of others completely miss: how do you make sure the input data is actually trustworthy before the model ever touches it?

I think of OpenGradient’s Data Nodes like a customs checkpoint before a clean room in a lab. The clean room is protected, controlled, and carefully sealed off. But without a checkpoint at the door, anything can still get in. And once it’s inside, no matter how precise the process is after that, the outcome is already compromised.

That’s what Data Nodes are doing. They take outside data like asset prices, API feeds, and market information, and run it through a trusted enclave before any model can use it.

That part matters to me because most whitepapers on on-chain AI barely deal with it. They focus on output verification and act like that solves everything, but it doesn’t. Garbage in, garbage out never stops being true just because the inference was verified. A perfect ZKML proof on bad input is still just bad output, only now it’s proven. @OpenGradient