Most "on-chain AI agent" narratives stop at the agent layer. @OpenGradient is solving the harder problem underneath: how does a smart contract actually trust an AI model's output?
Their answer is Hybrid AI Computing — combining GPU nodes, zkML proofs, and TEEs so inference is verifiable on EVM chains like Base. The numbers prove it's not theoretical: 2,000+ models hosted, 2M+ verifiable inferences, and 500K+ zkML proofs already generated.
The real insight? Most AI tokens will be useless utility tokens with no demand sink. $OPG actually pays for AI inference compute, secures the network through staking, and rewards node operators. With a fixed 1B supply and backing from a16z crypto and Coinbase Ventures, the fundamentals are quietly strong — even as price sits -3.0% on the day.
Our algo's full open track record — wins and losses alike — is linked in our bio if you want to see how we trade setups like this transparently.
What's your take: will verifiable inference become the base layer every on-chain AI agent eventually needs?
$OPG #OPG
Their answer is Hybrid AI Computing — combining GPU nodes, zkML proofs, and TEEs so inference is verifiable on EVM chains like Base. The numbers prove it's not theoretical: 2,000+ models hosted, 2M+ verifiable inferences, and 500K+ zkML proofs already generated.
The real insight? Most AI tokens will be useless utility tokens with no demand sink. $OPG actually pays for AI inference compute, secures the network through staking, and rewards node operators. With a fixed 1B supply and backing from a16z crypto and Coinbase Ventures, the fundamentals are quietly strong — even as price sits -3.0% on the day.
Our algo's full open track record — wins and losses alike — is linked in our bio if you want to see how we trade setups like this transparently.
What's your take: will verifiable inference become the base layer every on-chain AI agent eventually needs?
$OPG #OPG