For most of Web3’s history, the bottleneck has been execution. The prevailing assumption was that if blockchains could process transactions cheaply and quickly, everything else would follow. Rollups, parallel execution, and optimized consensus solved that part. What they did not solve is the data layer the place where applications, and soon autonomous agents, actually keep what they learn, use, and produce.
AI is forcing that shift faster than anyone expected. Modern AI systems generate and consume massive amounts of data: embeddings, conversation logs, feature sets, fine-tuning samples, model artifacts, and private user context. None of this fits inside a blockchain’s native storage mechanism. Worse, none of it can safely live in public view. For AI to exist inside decentralized environments, it needs somewhere to put data that is (1) durable, (2) private, and (3) cryptographically verifiable without being revealed.
Walrus has emerged as the first protocol on Sui to supply that missing requirement.
The AI Workload Is Not Stateless
Most blockchain applications behave like calculators: they act on inputs and forget the result until the next transaction. AI agents do not work that way. Their behavior depends on memory. They need:
historical context,
stored embeddings,
user profiles,
datasets,
cache buffers,
and long-lived artifacts.
Without state they don’t improve, adapt, or coordinate. Without private state they cannot interact with real users or enterprises. Cheap blockspace alone cannot satisfy those needs.
Walrus provides the state layer. More specifically: encrypted, off-chain, verifiable blob state.
Why Blockchains Cannot Be the Storage Layer
People often propose storing AI data directly on-chain, but that fails for three structural reasons:
1. Cost — blockchains price data by scarcity, not volume
2. Latency — blockchains are optimized for consensus, not retrieval
3. Privacy — blockchains are transparent by default
Walrus sidesteps all three by splitting the stack:
Sui handles execution + settlement
Walrus handles data + privacy + availability
The connection point is cryptographic proofs, not raw bytes.
Encrypted Blobs Solve the Agent Privacy Problem
AI agents need to carry private user context. This may include:
medical data
financial preferences
corporate knowledge
personal profiles
behavioral patterns
Storing that openly destroys user trust and violates compliance. Storing it off-chain in centralized clouds introduces a trust bottleneck. Walrus makes it possible to store encrypted blobs that neither validators nor storage nodes can read.
Access is mediated through keys, not backend trust.
Availability Without Disclosure
One overlooked requirement for AI is the ability to verify that a resource exists before committing to use it. In centralized clouds, this is trivial the provider guarantees availability. In decentralized systems, that assumption does not exist. Walrus reconstructs it through:
erasure coding (fragment resilience)
periodic PoA proofs (availability)
staking (economic penalties)
prepaid storage (sustainability)
This matters because agents cannot interact with datasets that may or may not exist. They need determinism.
Walrus gives them probabilistic availability backed by economic guarantees, not hope.
Retrieval Is the Real Bottleneck
People assume compute is the expensive part of AI. In many workloads, retrieval dominates cost. Models spend substantial time waiting for data rather than thinking. Centralized providers solve this through optimized data locality. Walrus approaches it differently: it splits data into fragments that can be fetched in parallel from different nodes.
AI systems can reconstruct data faster and more reliably without trusting a single endpoint. This changes the incentives for decentralized compute as well.
Why Agents Need Private Storage More Than L1 Blockspace
As AI agents become autonomous, they stop being passive consumers and become economic participants. An agent may:
pay for storage
buy datasets
exchange private artifacts
negotiate data access
archive logs for audits
Blockspace does not enable these workflows. Private data does. The agent economy runs not on blockspace, but on memory and negotiation, both of which require encrypted, persistent storage.
Walrus makes that possible.
The WAL Token as Coordination Mechanism
WAL is not a speculative wrapper around the protocol. It coordinates:
who stores data,
for how long,
under what guarantees,
and at what penalty for failure.
Agents do not need to trust storage nodes. They only need to trust the economic math that keeps nodes honest.
The Broader Implication: Data Becomes the Valuable Asset
In the early crypto era, tokens were the scarce object. In the AI era, data is. Models are converging, compute is commoditizing, but data remains differentiated and defensible. Walrus positions Sui for this shift by making private data a native resource, not an external dependency.
Cheap execution matters, but without data, execution is sterile. Walrus supplies the memory that makes compute meaningful.