Centralized AI clouds create a dangerous black box. You send your data, they run inference, you trust the output. But what if the model silently changed its logic?
@OpenGradient solves this with verifiable on-chain AI. Its HACA stack combines GPU nodes, zkML proofs, and TEEs so every inference is mathematically provable. With 2M+ verifiable inferences and 500K+ zkML proofs already generated, the trust assumption is removed entirely.
Built on Base and backed by a16z, this is how AI computation becomes transparent. $OPG powers inference, staking, and governance across the network.
Our algo's full open track record, wins and losses included, is in our bio.
What breaks first: centralized AI trust or decentralized infrastructure scaling? $OPG #OPG
@OpenGradient solves this with verifiable on-chain AI. Its HACA stack combines GPU nodes, zkML proofs, and TEEs so every inference is mathematically provable. With 2M+ verifiable inferences and 500K+ zkML proofs already generated, the trust assumption is removed entirely.
Built on Base and backed by a16z, this is how AI computation becomes transparent. $OPG powers inference, staking, and governance across the network.
Our algo's full open track record, wins and losses included, is in our bio.
What breaks first: centralized AI trust or decentralized infrastructure scaling? $OPG #OPG