Recently, I've been pondering a phenomenon: why are chatbots and AI investment tools increasingly prone to giving outrageous conclusions? On the surface, it seems to be a model issue, but in reality, the root often lies in the data.

I tried asking about some basic data, and the results were ridiculously off — only to find out upon verification that the information was fundamentally wrong. Where's the problem? According to industry data from 2025, over 37% of AI-generated errors directly stem from contaminated training data or data that cannot be traced back. This is not a small figure.

Imagine an investment model providing ambiguous reasons, a chat assistant confidently spouting nonsense, and you have no idea where the information source is. Who modified it along the way, how good is the data quality — it's basically a black box. It's like eating spoiled takeout; you can't pinpoint which link in the chain caused the problem.

An industry consensus is forming: AI competition is no longer just about model parameters; the key is whether the data is "clean" and verifiable. This presents an opportunity.

Recently, I observed actions from a leading public chain ecosystem, where they are using a set of technologies to address this issue. Among them is a protocol dedicated to data verification and storage, with an interesting approach — not just storing data, but aiming to be the "notary office" for data in the AI era, making every piece of information traceable and verifiable. This direction is worth paying attention to because it’s truly the way to solve AI credibility issues.