If we’re serious about on-chain AI and autonomous agents, the bottleneck isn’t TPS it’s data: where it lives, who owns it, and whether it’s still available when an app needs it.
That’s why I’m watching @Walrus 🦭/acc closely. #Walrus is positioning itself as a data layer built for big blobs (datasets, media, model outputs) where availability is verifiable and economically enforced not “trust me bro” pinning.
Here’s what stands out:
Sui as the control plane: Walrus isn’t trying to be another L1. It uses Sui for coordination, on-chain objects/metadata, and settlement meaning stored blobs become composable, on-chain resources that apps can reason about.
Proof of Availability (PoA): availability becomes an on-chain certificate. That turns storage into something programmable contracts and apps can check “is this blob available and for how long?” instead of guessing.
Cost model designed for scale: Walrus is built around erasure coding and efficient replication (think cloud-style efficiency vs full replication everywhere), so storing large files is actually feasible for real products.
Token utility with real hooks: $WAL is used for payments (priced to keep costs stable in fiat terms), delegated staking for security, and governance plus a 10% subsidy allocation to bootstrap adoption early.
Deflationary pressure (with purpose): burning is tied to behavior penalties on noisy short-term stake shifts and (once enabled) slashing for low-performance nodes. That’s not “burn for hype”; it’s burn that reinforces reliability.
My simple take: if AI apps are going to run on-chain, they need data that’s owned, provable, and still there tomorrow. Walrus is building directly for that reality not as a narrative, but as infrastructure. $WAL is the coordination + security layer behind it.