Technical solutions are often hard to evaluate clearly in the early stages. Running smoothly, having a decent user experience, and keeping costs reasonable are enough to sustain for a while. The real test comes later—when users flood in, data explodes, and call frequencies soar, those overlooked details in the early days will surface one by one, becoming critical to survival.

The data layer is exactly in this position.

In many applications, data seems to exist automatically as a background element. Everyone is focused on making the frontend smoother, smarter features, or more sophisticated financial models, but few ask a simple question: if this data needs to stay there long-term, be called frequently, and flow across applications, can the current structure handle it? It may not be obvious in the short term, but over a longer timeline, it’s unavoidable.

The reason I continue to pay attention to Walrus is that it has been putting this issue on the table from the very beginning.

No complicated packaging, no flashy narratives—just a relatively pragmatic approach: in a decentralized environment, how can we store and distribute large-scale data more efficiently and reliably? It sounds not new, but truly mastering the three elements of cost, stability, and scalability is no small feat.

Looking at it from another angle, if we see the entire system as a building that keeps adding layers, many solutions start by attaching the outer walls first and then reinforcing the structure inward; Walrus’s logic is the opposite—it first lays a solid foundation and load-bearing pillars, then considers stacking upwards. This sequence may not be popular initially, but from a long-term operation perspective, it’s a more sound approach.

As AI-related applications begin to take shape, the advantages of this mindset will become increasingly apparent.