Prediction markets have become a focus again in the past two years. Especially during last year's US election, the daily trading volume of a leading prediction platform soared to several hundred million dollars, which is enough to demonstrate that the business logic of this track is valid.

But there is a big pitfall here—who determines the truth of the results?

In prediction markets, if you bet on whether a bill will pass or who will win a match, at the reveal moment, if the results are judged manually, there is room for manipulation. If relying on on-chain data, you also have to worry about attacks. It’s a deadlock.

Projects like $AT Oracle are here to break the deadlock. They provide decentralized event result verification services through multi-source data aggregation and AI validation. The key is that the entire process is transparent and verifiable, and anyone can verify the results.

What’s most interesting is that the combination of prediction markets and AI is creating new application scenarios. For example, some AI agent platforms have AI robots that can autonomously trade on-chain, participate in DeFi, and even place bets in prediction markets. Sounds impressive, but AI decision-making requires high-quality information input—this is exactly where Oracle comes into play.

The specific process is as follows: AI agents subscribe to multi-dimensional data streams, including price quotes, on-chain indicators, social media sentiment, and news events, then analyze this information with large models to make trading decisions. It sounds simple, but the technical complexity is not low. Because AI models are easily misled by junk data—you feed them false news or manipulated prices, and their judgment will collapse. That’s why the credibility of data sources becomes the most critical link in the entire chain.