Once high-performance public blockchains are truly operational, the bottleneck is often not performance itself, but whether they can run continuously and stably. When an application goes live and starts serving real users, data growth becomes uncontrollable. Complexity skyrockets, and the frequency of state updates sharply increases. At this point, the problem arises—relying solely on on-chain storage is simply not sustainable.

The emergence of infrastructure projects at the data layer has a clear background: a large amount of data cannot be stored on-chain, which is a consensus. But the contradiction lies in not losing trustworthiness. On-chain data cannot verify off-chain data, and as application scale increases, risks become more concentrated. How to break this dilemma?

From a technical perspective, the solution is division of labor. On-chain only needs to judge data results, without storing all the details. This model may not show its value in the early stages of application development, and might even seem redundant. But when concurrency explodes, and business complexity and user volume surge simultaneously, it becomes an essential requirement.

This is also why the value of such data layer components has little to do with any particular popular application; instead, it depends on whether the entire ecosystem has entered a scale-up phase. Once a tipping point is reached, they shift from optional technical solutions to indispensable prerequisites for the system.

Currently, the public chain ecosystem is still in the stage of rapidly deploying applications. But when this phase passes and the system begins to pursue stability and scalability, the true value of data layer infrastructure will gradually emerge. By then, its irreplaceability will no longer be theoretical but a reality.