I’ve been thinking a lot about how Web3 is changing at a practical level, not a narrative one. The shift that stands out most to me lately is this: data is no longer short-term. Apps aren’t just executing transactions and moving on. They’re storing things people expect to exist tomorrow, next year, and well beyond that. That’s the context where
@Walrus 🦭/acc keeps making sense to me.
Look at how products are being built right now.
AI agents don’t just run once and stop. They keep memory. They reference past interactions. They build context over time. That means the data they generate is part of the product itself. If that data disappears or becomes unreliable, the system loses value fast. Centralized storage works until it doesn’t, and when it fails, the whole agent stack feels fragile.
#walrus fits here because it gives those systems a way to store data without tying everything to one provider or pushing heavy storage onto execution layers. It treats persistence as something intentional, not something bolted on later.
Health-related platforms show a similar pattern.
Even outside of regulated medical systems, health tech deals with information people expect to be durable. Research data, anonymized records, device readings, personal history. The requirement is simple and strict at the same time. The data needs to remain available, and it needs to be provably unchanged. Relying on one company to hold that data forever is risky. Companies pivot. Services shut down. Access rules change. Walrus doesn’t remove all of those risks, but it reduces how much trust is placed in any single party. That matters when data needs to outlive the platform that created it. What I find telling is that these kinds of teams don’t choose infrastructure casually. AI and health platforms aren’t chasing trends. They’re trying to avoid failure modes that show up later and cost real money and credibility. When they test something like Walrus, it’s usually because existing setups are already showing cracks.
Zoom out and you see the same pressure across Web3.
NFTs are no longer static images. They rely on metadata that has to remain accessible.Games aren’t demos anymore. They’re persistent worlds with ongoing state.Social platforms generate content users expect to stick around.
All of this creates data that does not disappear when markets cool down. Trading activity can drop. Speculation can slow. Data keeps accumulating.
That’s where a lot of earlier assumptions break.
Execution layers are good at computation. They’re not great at storing large amounts of data forever. Putting everything on-chain gets expensive quickly. Putting everything off-chain brings back trust assumptions most teams are trying to avoid. A dedicated decentralized data layer sits in between those extremes. That’s the role Walrus is trying to play.
It’s also why I don’t think about
$WAL as a token tied to a single sector or trend. I see it as exposure to whether this data layer actually becomes useful. If AI agents rely on it, usage grows. If health platforms rely on it, usage grows. If games, NFTs, and social apps rely on it, usage compounds.
That kind of growth is quiet. You don’t see it explode on a chart overnight. You see it show up as dependency over time. None of this guarantees success. Storage is competitive. Performance and cost still matter. Teams will move on quickly if something doesn’t hold up under real use. Walrus still has to earn trust the hard way. But I pay attention when infrastructure starts getting tested in places where data loss is unacceptable. That usually means the problem is already real, not theoretical. If Web3 keeps moving toward AI-driven systems, real-world data, and applications people actually rely on, storage stops being a side concern. It becomes foundational. Walrus feels like it’s positioning itself for that reality, even if most people aren’t focused on it yet. That’s why I’m still watching walrusprotocol closely.