In today's rapidly developing field of artificial intelligence, an unexpected phenomenon is quietly occurring. Many people focus on developing AI applications and models, yet overlook a potential goldmine: providing high-quality data for AI.

Recently, I heard an astonishing story. One of my high school classmates earned a considerable profit just by participating in the Chainbase project, providing data and conducting verification. This made me reflect that in the AI era, the real opportunity may not lie in creating complex algorithms, but in providing AI with the essential nutrients it needs most—high-quality data.

The emergence of platforms like Chainbase is aimed at addressing a key issue faced by AI: obtaining reliable, real-time, structured data. These platforms support multiple blockchains and provide a variety of data, from transaction prices to network activity, with extremely low latency, some even achieving updates in seconds.

More importantly, these platforms have introduced innovative verification mechanisms to ensure the credibility of the data. At the same time, they have also designed incentive systems to encourage users to upload data, participate in verification, and even reward developers for using this data. This model not only addresses the data needs of AI but also creates new income sources for participants.

This phenomenon reveals a often overlooked link in the AI ecosystem: the data supply chain. While large language models and complex algorithms often take the spotlight, high-quality, timely data is the true lifeblood of AI.

With the continuous advancement of AI technology, we may see more platforms similar to Chainbase emerging, which will play a key role in connecting data providers and AI systems. This not only creates new opportunities for individuals but may also reshape the entire AI industry chain.

In this rapidly changing field, it is important to maintain an open mindset and focus on the development of the entire ecosystem. Perhaps the next driving force of the AI revolution will not be algorithm engineers, but those participants who can provide critical data.