Honestly, whenever I see someone hype up a oracle project as "the future will definitely be like this or that," I instinctively step back—not because these projects are inherently problematic, but because I don't want to be bitten by my own certainty.

Now my habit has reversed: I evaluate any infrastructure as a "system that will eventually fail." It sounds a bit pessimistic, but I think this is the most honest attitude. It's not about how shiny it looks when it runs smoothly, but whether it can stand firm when it crashes—faults in oracles are never low-probability events, just differences in scale. Small issues can be isolated, but a major incident could be fatal.

Why am I saying this? Because adding AI to oracles directly expands the risk surface. Traditional oracles mainly get stuck on "price source manipulation," but projects that consume unstructured data introduce several layers of hidden dangers: the information itself can be polluted, false evidence can be present, texts can be cleverly manipulated, and biases in the model itself. To put it plainly, they are not only easier to exploit but also have more elaborate ways to trip up. So my logic is the opposite—don't rush to hype it up; first imagine it will fail, then see if it has the ability to save itself.

My judgment is that if an oracle is to survive long-term, the strongest moat is probably not how accurate its reasoning is, but its emergency response capability. In other words, after an output error, can it quickly cut losses, assign responsibility, rollback, re-examine, and ultimately still earn the developer's trust to continue using it?

I focus on three practical issues:

First, when off-chain evidence contradicts each other, does it dare to make a decision? I would prefer it to output low confidence levels, or even refuse to settle and explain the reasons, rather than force an output just to appear "decisive."