Recently, I discovered a rather interesting phenomenon - the two seemingly unrelated fields of AI and Blockchain are experiencing a wonderful chemical reaction in the Data Layer. Particularly when we turn our attention to those projects that are truly doing infrastructure, players like Chainbase in this data track really stand out.

How chaotic is the blockchain data now? There are more than 50 mainstream public chains, each with different block structures and smart contract standards. Not to mention that more than 2 million transaction data are added every day, and this data is like documents thrown into a shredder, scattered across different chains. But what Chainbase does is quite clever; they don't simply create a data crawler, but instead build a complete "data refining" system. According to community feedback, their real-time indexing technology can improve data query speeds to sub-second levels, which is quite impressive in a decentralized environment.
The platform has currently processed over 800 million data requests, with an average daily API call volume exceeding 3 million. Behind these numbers are real-time data applications such as DEX aggregators and on-chain analysis tools. Developers have mentioned that using Chainbase's services can save nearly 70% of operational costs compared to building their own nodes.
The most noteworthy concept they proposed now is the "Hyperdata Network". In simple terms, it aims to reorganize on-chain data into a format that AI models can directly digest using a distributed approach. It is important to note that currently, over 90% of the blockchain datasets used for AI training are still provided by centralized institutions. If Chainbase can truly open up this decentralized data pipeline, it may give rise to a new generation of on-chain AI applications.
From an investment perspective, these types of infrastructure projects often exhibit a strong Matthew Effect. Once data services form a network effect, the migration costs become very high. Currently, Chainbase has connected to 12 mainstream chains, including Ethereum and Solana, and the technical route is cleverly chosen—focusing on indexing rather than storage, which avoids direct competition with storage chains like Arweave. As more AI projects begin to experiment with on-chain data training, the demand in this niche market may experience an explosion.

{future}(CUSDT)