In the blockchain world, the most dangerous thing is often not the spectacular collapses that shake the sky, but the silent leaks that go unnoticed. A tiny deviation in decimal points, a delayed confirmation, or even a sudden failure of a data source—these seemingly minor issues can lead to major disasters. Fundamentally, all problems stem from the same root: the disconnect between the perfect logical world of smart contracts and the chaotic external environment of reality.

This disconnect point is precisely the responsibility of oracles.

The core problem that oracles need to solve boils down to one word: trust. Blockchain technology frees you from dependence on trading counterparts, but you still have to trust the data fed into the contracts. Every price data point, every asset proof, appears to be native on-chain, but in reality, they secretly reintroduce trust. Traditional solutions rely on a few data providers, concentrating risk too heavily. Some current explorations take a different approach: multi-party off-chain data collection, combined with technical methods for cross-verification, and only then anchoring the results on-chain. The advantage of this approach is not to claim to eliminate risk entirely, but to bring risks from the shadows into the light, making malicious behavior more costly.

From the perspective of data push logic, we can also see this pragmatic attitude. Some adopt continuous pushing, updating regularly like a heartbeat; others pull data on demand, querying only when needed. It may seem like a small detail, but it actually acknowledges a fact: different scenarios have vastly different requirements. Forcing a single solution onto all situations only leads to network waste.

The most interesting development is that some projects are now beginning to incorporate AI into the verification process. Think about it—data on the chain is no longer just pure numbers; various reports, proofs, and documents are being uploaded. These unstructured data are exactly what AI excels at handling. But here’s the problem—AI can make mistakes, and it can be very covert about it; sometimes, you might not even notice. This brings us back to the old question: how do you trust the verifier itself?