In the crypto market, the AI track has moved from a purely conceptual stage to practical application. However, interestingly, when discussing DefAI, people often focus on surface-level aspects such as Computing Power leasing or model training, and rarely mention a real underlying bottleneck—how data is organized and retrieved.

To understand this issue, one must first recognize a reality: AI is essentially a data-hungry application. The architecture of traditional blockchains (such as Ethereum) is based on key-value storage, which simply means a structure where one key corresponds to one value. This logic works fine when processing a single transaction, and the efficiency is acceptable. However, when it comes to complex data queries and multi-dimensional relational analysis—which is precisely what AI and advanced DeFi protocols require—traditional blockchains appear to be inadequate, as they not only have low query efficiency but also incur high costs.

It is precisely because of this bottleneck that many AI projects on the market have adopted a workaround: data is stored off-chain, and only a proof is kept on-chain. What is the cost of doing this? Semi-centralization. This clearly goes against the original intention of Web3.

So how do we solve this? There is a project called Chromia($CHR) that offers an interesting idea—rethinking this problem with the concept of "relational blockchain."

In simple terms, the difference between relational databases and key-value stores is that the former can handle complex associations between multiple tables and supports flexible query logic. Chromia has brought this approach to the blockchain. What does this mean? It means you can perform multi-dimensional data association analysis directly on the chain without needing to transfer data off-chain.

From an architectural perspective, this is a significant upgrade. On-chain AI applications are no longer confined to simple data structures and can handle more complex business logic. For DeFi protocols, this also opens up new possibilities - more complex risk assessment and more precise liquidity management.

Of course, this innovation also brings new challenges. How to maintain reasonable performance for complex data queries while ensuring decentralization is the core issue that relational blockchains need to address.

But anyway, this direction is worth paying attention to. Because it touches on the real pain points of the integration of blockchain and AI, rather than staying at the level of conceptual hype.