Why does "verifiable AI" actually matter? Most AI today is a "black box"—you send a prompt and trust the output without knowing if the model was tampered with. @OpenGradient is changing this by bringing verifiable on-chain inference to Base.
By combining GPU nodes with zkML proofs and TEEs (Trusted Execution Environments), they ensure that the AI output you receive is exactly what the model intended, without hidden biases or manipulation. With over 500K zkML proofs already generated, this isn't just a concept; it's a functional infrastructure for agentic reasoning. Backed by a16z and Coinbase Ventures, they are solving the trust gap in decentralized AI.
While we track the tech, our algo tracks the charts—our full open track record of every win and loss is available in our bio.
Do you think zkML is the key to making AI truly decentralized?
$OPG #OPG
By combining GPU nodes with zkML proofs and TEEs (Trusted Execution Environments), they ensure that the AI output you receive is exactly what the model intended, without hidden biases or manipulation. With over 500K zkML proofs already generated, this isn't just a concept; it's a functional infrastructure for agentic reasoning. Backed by a16z and Coinbase Ventures, they are solving the trust gap in decentralized AI.
While we track the tech, our algo tracks the charts—our full open track record of every win and loss is available in our bio.
Do you think zkML is the key to making AI truly decentralized?
$OPG #OPG