Many people when talking about Walrus, their first reaction is to compare it directly with traditional decentralized storage projects, and then start worrying about "cost performance." But this line of thinking is actually off track. Walrus is not aiming to be "a cheaper blob storage"; what it truly wants to do is to plug the trust loopholes in Web3 data from a structural level.

State verification and trusted computation are already mastered by blockchain technology; however, the data layer has always been a shortcoming. The harsh reality is: on-chain storage cannot hold that much data, so business data is either stored on centralized servers or entrusted to some storage networks with insufficient guarantees. It’s like installing an untrusted black box inside a highly trusted machine—the notion of "permanent and unchangeable" is all built on the most fragile assumptions: that the data remains alive.

What Walrus aims to do is to make this black box transparent. It’s not simply about "being able to store data," but about ensuring "it has always been there and has not been tampered with." How? Through erasure coding, lightweight replication, and periodic cryptographic proofs, storage nodes must continuously submit evidence that they are indeed holding the data shards. Verifiers can check these proofs at very low cost, and any cheating behavior will be immediately exposed and penalized. This mechanism provides on-chain data with a level of availability guarantee close to native chain standards for the first time.

What does this mean for application developers? You can confidently host large-scale data such as NFT metadata, game assets, user content, AI training set indexes, and social histories in a decentralized environment, without worrying about single points of failure or malicious data deletion. The entire process remains auditable and composable, truly turning data into a "trustworthy primitive" that on-chain applications can rely on.

But reality is also harsh: it came too early, and the underlying architecture is not yet robust enough. Currently, the on-chain ecosystem is still far from the stage where "data availability is a matter of life and death." Most applications are still using centralized solutions for convenience. For Walrus to grow, it needs to patiently wait for the complexity of applications to increase, and for scenarios with extreme requirements for data integrity—such as on-chain AI, decentralized social networks, and permanent identity systems—to truly explode.

From an investment perspective, this is not a story of short-term rapid gains. Its growth logic is entirely driven by actual usage, relying on network effects rather than emotional cycles. The tokenomics are also relatively restrained, mainly tied to real storage demand, not relying on deflation or destruction to artificially create scarcity.