Many projects do not show obvious reactions in the first few months, and the real issues often only become apparent after 6-12 months.

The reason is actually quite straightforward. User behavior data, status records, logs—these things seem to only increase by a few tens of KB daily, but over a year, they accumulate to a scale of 10-30GB. Traditional decentralized storage begins to show its passive nature at this stage—each update requires rewriting, historical versions pile up continuously, and reference relationships become increasingly confusing.

How about a different approach? Consider "long-term accumulation" as a fundamental premise from the start. The identity and references of objects remain fixed, but their states can continuously evolve, rather than generating a new batch of objects every year. What are the benefits of this design?

From public data, protocols like Walrus demonstrate this: a single object supports data scales in the MB range, the same object can undergo multiple state updates without changing reference relationships, and data is redundantly stored across multiple nodes, maintaining availability above 99%.

Realistically speaking: once data scales enter the "time-driven growth" phase, the value of this architectural approach may be more critical than simply pursuing low cost. However, the prerequisite is that the network node scale can expand accordingly; otherwise, accumulated historical data may become a source of system pressure.