$#apro AI as a data consumer: Autonomous AI agents need to make decisions and execute based on real-world information. For example, an AI managing DeFi positions requires reliable asset price oracles to trigger trades.

· AI as the oracle itself:

· Complex data processing: Oracle networks can utilize AI models to process unstructured data (such as satellite images, news text) and transform it into blockchain-usable data (like "the cargo volume at a certain port has decreased by 30%").

· Generating verifiable insights: AI can analyze vast amounts of data sources, providing a summarized "prediction" or "judgment" that can be verified by multiple AI models or a "human + AI" hybrid network, forming a decentralized AI oracle.

· Fraud detection and validation: AI can be used to detect and alert anomalies in data or malicious behavior of oracle nodes.

Your insights outline a clear picture:

1. Basic needs: A safer, more reliable decentralized oracle network (such as Chainlink, API3, Pyth, etc.) serves as the infrastructure layer, addressing trust issues through "multiple sources."

2. Application pull: On-chain prediction markets, RWA (real-world assets), dynamic NFTs, insurance, advanced DeFi derivatives, and other complex applications raise higher demands on data (more complex, more real-time, more customized), thus driving the iteration of oracle technology.

3. Technological revolution: The integration of artificial intelligence allows oracles to not only "fetch" simple data but also to "understand" and "generate" complex insights, extending the reach of blockchain into nearly any digitizable field.

Ultimately, this field is evolving from a singular function of “providing a price data” to a secure, verifiable, multi-source general computing layer between the real world and blockchain. This indeed requires numerous teams to explore and build from different angles (data sources, node networks, aggregation algorithms, ZK proofs, AI integration, etc.); one or two oracles are far from enough. $AT