Recently, I did something "counterintuitive." I didn't jump on the hype or retweet with the crowd; instead, I put a project into a real-world comparison frame and started dissecting it. I benchmarked it against two groups—traditional oracle veterans and those data-layer projects that are branding themselves with AI.

After this whole process, I suddenly realized a question: the core betting point of this project might not be the words "oracle" at all. What it truly aims to do is to transform on-chain data from "easy to use" into "able to explain clearly when something goes wrong."

It sounds not very sexy. But the more I ponder, the more I feel that by 2026, the truly valuable infrastructure will be these unsexy things.

First, look at the traditional oracle camp. I have to say, they are really strong. But what makes them strong? Essentially, they solve a standardized need—quotes, quotes, and more quotes. They are needed for settlement, leverage, and derivatives, and of course, the more stable the data, the better.

Here's the problem. If we really put this into real-world business scenarios, just having price data is simply not enough. What is the real headache in the real world? It’s the authenticity of vouchers, whether trading rules are being followed, whether the audit chain is complete enough, and whether responsibility can be pinpointed when something goes wrong.

Does the invoice corresponding to a certain transaction exist? Has a payment truly been settled? Has the status of an asset been altered? These things can't be fixed just by increasing "price update frequency."

Now, look at those AI data-layer projects. Recently, I’ve become a bit numb to them. The common routine is a pile of jargon and overly grand narratives, but once you dig deeper, you find they are hollow inside.