The Question Behind OPG Is More Interesting Than the Price
Lately, whenever I see people talking about OPG, most of the conversation seems to circle around trading activity and price action.
What keeps pulling my attention back is a different question entirely.
How do you actually trust an AI output if you have no way to verify how it was generated?
Most AI systems today still operate on a trust model. You send a request, receive an answer, and assume everything happened as promised behind the scenes. OpenGradient seems to be approaching that problem from another angle.
From what I understand, inference nodes generate the output, while full nodes verify it using TEE attestations and ZKML proofs before the result is recorded on-chain. The goal isn't just to produce AI responses, but to make them independently verifiable.
That's where OPG fits into the system through inference payments, staking, rewards, and governance.
What I'm still unsure about is whether this can scale efficiently.
Verification sounds valuable, but value and adoption aren't always the same thing. ZK-based systems aren't exactly known for being cheap.
So the metric I'm paying attention to isn't trading volume. It's whether real paid inference demand starts growing faster than speculation, and whether staking participation remains strong once incentives become less attractive.
That difference will probably tell us more about the network than any leaderboard ever could.