Smart money is shifting! When AI decision-making takes over your trades, this overlooked "trust layer" is the real key to wealth.

When the model begins to decide your buy and sell actions, assess risks, or even manage assets, a more fundamental question arises: How can you be sure its calculation process is honest? Can the results be traced and verified? Is data privacy secure? If the answers are vague, then no matter how powerful the model is, it’s just an unauditable black box of trust.

Market observations point out that a project called OpenGradient is attempting to solve this underlying trust issue. It is not aiming to create another AI application but is building a set of infrastructure that makes AI computations verifiable, auditable, and settleable. The project was born from a well-known venture capital crypto startup accelerator and has secured approximately $9.5 million in funding, including investments from the VC itself and $COIN-related investment institutions.

Its team is oriented toward engineering practice. The CEO previously worked at hedge fund Two Sigma, while the CTO comes from data analytics company Palantir, with backgrounds at Google and Amazon. Blockchain engineers have led core development of a cross-chain protocol.

The core of OpenGradient is called the “Open Intelligent Network,” with a technical architecture based on hybrid AI computation. It places heavy AI inference tasks off-chain, with dedicated nodes generating verifiable proofs, while on-chain nodes focus on verifying these proofs rather than re-computing, balancing performance and cost.

There are different roles within the network: full nodes handle consensus and validation; inference nodes provide computing power; data nodes acquire trusted external information; and the storage layer is responsible for hosting models, data, and proofs. The storage layer, called Walrus, is a DePIN storage solution within the $SUI ecosystem, ensuring all data’s permanent availability and resistance to censorship—an essential part of trustworthy AI computation.

This architecture adopts an asynchronous verification design, where users first receive inference results, and verification and settlement are completed afterward. Its blockchain layer is based on CometBFT consensus, compatible with Cosmos SDK and EVM, responsible for node registration, proof validation, payment processing, and ledger management.

How to prove that AI is not deceiving? The project offers a tiered “trust menu”: trusted execution environments based on secure hardware, zero-knowledge proof machine learning for high-risk scenarios, and lightweight schemes that only perform signature verification. Any verification must be confirmed by more than two-thirds of network validators before being finally recorded.

Centered around this core capability, the project has built a series of products. The Model Hub is a decentralized model repository that uses the Walrus network to ensure storage. x402 Gateway allows paid access to AI services. MemSync provides AI agents with long-term memory. Twin.fun is a digital avatar trading marketplace.

The economic model is driven by the $OPG token, designed to bind all key activities within the network, including paying inference fees, monetizing models, staking, and application access. Its total supply is fixed at 1 billion tokens, with 40% allocated to the ecosystem, and the team and investors have lock-up periods. The token generation event is scheduled for April 23, and it will be listed on relevant trading platforms.

The logic is quite clear: once AI deeply involves financial flows and key decisions, “trust” must become a verifiable commodity. Of course, real-world challenges still exist: do most application scenarios truly require this verification? Are users willing to pay extra for it? These questions remain unresolved. What OpenGradient is doing is not optimizing AI itself but attempting to redefine how we trust AI.

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