Up to now, I have become more cautious. Not suddenly conservative, but after stepping into a pit — it's too easy to replace "this thing should be awesome" with "is it really awesome now." I’ve been burned by this before: when you treat something that hasn't happened yet as if it already has, you tend to double down when it's time to retreat.

So this time, I’ll look at it from a different perspective. No post-mortem, no discussion of frameworks; instead, using a proof by contradiction: suppose this project ultimately fails, at which point would it first break down? Conversely, as long as it can get past these hurdles, even if it stirs up trouble in the short term, it’s worth keeping an eye on as a long-term target.

Here’s the core conclusion upfront: success or failure doesn’t depend on the words "AI," but on "trustworthy." AI can be copied, but trustworthiness is hard to replicate. In scenarios where information is chaotic and structures are unclear, trustworthiness becomes more valuable, and the moat can truly exist.

The first point most likely to be exposed: consistency.

On-chain unstructured data sounds very advanced, but the first real test is straightforward: with the same piece of evidence, can different nodes reach the same conclusion? If they don’t match, how does the system converge?

I don’t want to hear the standard answer of "we have a consensus mechanism." I want to see specific operations—

Suppose node A says "risky," node B says "risk-free." Is the final decision made by voting? Or weighted by trustworthiness? Or is there an arbitration? Who does the arbitration? Who pays the fees? If in the end, the fallback is "trust a certain authoritative node," then this system is actually easy to expose.