Can we actually trust AI when it runs on a black box?
Most "AI coins" are just wrappers for centralized APIs. @OpenGradient is tackling the real bottleneck: verifiable on-chain inference. By combining GPU nodes with zkML proofs and TEEs (Trusted Execution Environments) via their HACA architecture, they ensure that the AI output you receive is exactly what the model produced, without tampering.
With over 500K zkML proofs already generated on Base, this moves AI from "trust me" to "verify me." For developers, the On-Chain AI SDK means they can finally monetize open-source models transparently. While the market sees steady volume around $4M, the real value lies in this shift toward decentralized, provable intelligence.
Our algo's full open track record of wins and losses is available in our bio for those who value transparency.
Do you think zkML is the missing piece for mass AI adoption in crypto?
$OPG #OPG
Most "AI coins" are just wrappers for centralized APIs. @OpenGradient is tackling the real bottleneck: verifiable on-chain inference. By combining GPU nodes with zkML proofs and TEEs (Trusted Execution Environments) via their HACA architecture, they ensure that the AI output you receive is exactly what the model produced, without tampering.
With over 500K zkML proofs already generated on Base, this moves AI from "trust me" to "verify me." For developers, the On-Chain AI SDK means they can finally monetize open-source models transparently. While the market sees steady volume around $4M, the real value lies in this shift toward decentralized, provable intelligence.
Our algo's full open track record of wins and losses is available in our bio for those who value transparency.
Do you think zkML is the missing piece for mass AI adoption in crypto?
$OPG #OPG