Today, let's not dwell on K-line trends. Instead, let's talk about something all crypto enthusiasts use but easily fall into traps with — oracles.

This thing is simple to explain: it’s the information transporter between blockchain and the real world. If smart contracts want to know spot prices, weather data, or match results—off-chain information—they rely on oracles to fetch and transmit it. Sounds straightforward, but the real problem lies beneath.

**The Most Frightening Hidden Risks**

The danger of oracles isn’t that they suddenly collapse, but that they silently fail. You see the market moving normally, the trading interface looks fine, but the data coming from behind the scenes has long been inaccurate. What’s the most heartbreaking scenario? When you want to trade, suddenly there’s no liquidity, and you can’t execute any orders. On the surface, everything seems prosperous, but in reality, the data and the market have already decoupled — what you see is just an illusion.

**The Root Cause: Incentive Mechanisms Are Distorted**

Many think this is a coding problem, but that’s not the case. The core issue is that incentives are skewed. Data nodes provide information not because it’s truly useful, but purely to earn rewards. The system’s criteria for evaluating nodes are even more absurd — it only cares about how long they’ve been online, completely ignoring whether the data is timely, accurate, or reliable. It’s like hiring a clerk who only cares if they clock in on time, ignoring whether the store’s stock is complete or prices are correct. In the end, the entire system gets dragged into a deep hole. By the time you notice the signs, the losses are already sealed.

**New Approaches to Break Through**

To address this pain point, some projects are starting to rethink their strategies. Instead of the passive mode of “I don’t care if you need it or not, I just push data onto the chain,” they’re shifting to an active pull mechanism — “whoever needs data pays to buy it.” They’ve also added layered update designs to make data supply more flexible and efficient. This way, incentives can be realigned — only high-quality data can earn money, rather than just relying on superficial familiarity.