$BZZ 【Tether CEO: Centralized AI Has Reached a Dead End, Will Promote Local AI to Practice Encrypted Self-Managed Concepts】PANews April 12 News, stablecoin issuer Tether CEO Paolo Ardoino posted on the X platform that "Artificial intelligence should be as open as freedom." He believes that the development path of centralized AI has reached a "dead end," and against the backdrop of large tech companies continuously building closed AI systems, Tether is moving in the opposite direction. The company will explore solutions to run AI models on local devices through QVAC, allowing users to autonomously control their data and computing power. This model also aligns more closely with the core of the crypto industry’s "self-custody"—that users should have full autonomy over their data and computational resources.

Dixie: Combining Tether’s QVAC local AI strategy and the current evolution of AI agents, we will deeply analyze the core needs and value positioning of decentralized storage (taking Swarm BZZ as an example) within the AI agent ecosystem.

This is no longer traditional "file storage," but rather the "long-term memory layer" and "collaborative consensus layer" of AI.

---

🧠 1. Core Needs: Upgrading from "Storage Layer" to AI's "Hippocampus"

QVAC emphasizes AI running on local devices, but this introduces a contradiction: limited local computing power, and devices may go offline or be replaced at any time. AI agents need a way to maintain "memory" and "personality" consistency across different devices and sessions.

This is where decentralized storage plays a crucial role—becoming the AI’s permanent, verifiable memory layer.

| Need Dimension | Traditional Cloud Storage | Decentralized Storage like Swarm | Value Proposition |
|------------------|---------------------------|------------------------------|-------------------|
| Memory Persistence | Relies on centralized servers; data lost if service stops | Content is permanently addressable, not dependent on a single provider, aligns with "self-hosting" | |
| Cross-Device Synchronization | Requires centralized account system | Based on DID (Decentralized Identity), AI can restore full memory on any device via private key | |
| Privacy and Ownership | Data owned by platform, can be used for training | Encrypted data storage, user/AI hold the only key, truly realizing "data autonomy" | |
| Verifiability | Cannot prove data has not been tampered with | Content hash can be verified on-chain, ensuring AI reasoning is auditable and traceable | |

Typical case: AgentDB protocol has enabled AI agents to pin "memories" directly to the IPFS network and achieve cross-device "hot migration" via IPNS. This is precisely the core scenario that Swarm can support.

---

🤝 2. Ecosystem Construction: Three Deep Needs of AI Agents for Swarm

Combining QVAC’s local-first architecture with the current Web3 AI ecosystem, AI agents’ needs for storage layers like Swarm can be refined into three levels:

2.1 Long-term Memory and Knowledge Graphs (Core Necessity)

· Need Description: AI needs to remember long-term interaction history with users, personalized preferences, task context, and be able to call upon them across different sessions.
· Swarm’s Fit: High. Swarm can serve as a cold storage layer, storing AI’s long-term memories (compressed chat history, knowledge vector databases). When AI starts, it pulls memories from Swarm into the local TEE environment.
· Ecosystem Mapping: Projects like MemorylAIer, AgentDB have already set IPFS/Storacha as default memory backend, proving this model is feasible.

2.2 Multi-Agent Collaboration and Verifiability (Differentiated Advantage)

· Need Description: When multiple AI agents collaborate on complex tasks, they need to share intermediate results, reasoning logs, evidence files, and the entire process must be traceable and tamper-proof.
· Swarm’s Fit: Extremely high. Swarm’s content addressing and on-chain verification capabilities are naturally suited for storing "collaborative evidence chains."
· Ecosystem Mapping: Swarm Network (Fact-Checking Protocol) has chosen Walrus to store fact-checking logs, media evidence, and consensus records of AI proxies, exemplifying a "verifiable AI" use case.

2.3 Model Distribution and Edge Computing Support (Long-term Potential)

· Need Description: QVAC enables smartphones to fine-tune large models, but these model files (like LoRA adapters) need distribution channels and integrity verification.
· Swarm’s Fit: Moderately high. Swarm can host decentralized distribution of model files, leveraging P2P bandwidth to reduce costs. Tether’s QVAC SDK already has built-in P2P mechanisms, aligning with Swarm’s bandwidth incentive model.
· Ecosystem Mapping: The DePIN+AI report indicates decentralized storage has become a key infrastructure for AI model distribution and dataset hosting.

---

🔮 3. Value Reconstruction and Valuation Logic of Swarm (BZZ) Under AI Narrative

Currently, BZZ is at a historical low (~$0.19), mainly affected by fierce competition in the storage track (Filecoin, Arweave) and slow ecosystem development. But the explosion of AI agents could become a catalyst for its valuation re-rating:

| Stage | Core Driver | BZZ Price Range Projection | Valuation Logic |
|---------|--------------|----------------------------|-----------------|
| Current (2026.04) | Ethereum ecosystem infrastructure | $0.18 - $0.38 | Reflects pure storage rent value, suppressed by competition |
| AI Memory Layer Validation | Killer AI agent applications (e.g., autonomous social assistants) adopting Swarm as memory backend | $0.56 - $0.78 | Premium from "AI data services" narrative, market expansion similar to AgentDB projects |
| Multi-Agent Collaboration Boom | Fact-checking, DeFi trading, content creation, multi-agent collaboration becoming mainstream, Swarm as "collaborative evidence chain" | $1.25 - $2.50 | Value capture shifts from storage to "verifiable computation," referencing Arweave’s AO supercomputing valuation logic |
| Ecosystem Maturity (2030) | AI agents as mainstream on-chain interaction partners, storage layer as Web3 standard | $5.00 - $10.00 | Based on AI agent economic scale (projected $8 trillion online expenditure), deriving valuation |

Core assumption: The revival of BZZ price depends not on technology alone but on whether an AI-native application (like autonomous social assistants, on-chain trading bots) emerges that uses Swarm as the default memory layer and scales.

---

⚠️ 4. Key Challenges and Observational Signals

· Competition landscape: Filecoin (FVM) and Arweave (AO) are actively deploying AI storage layers. Swarm needs to strengthen bandwidth incentives and deep EVM integration advantages.
· Developer experience: Whether a simple SDK like AgentDB can be provided, allowing AI developers to access Swarm as a memory layer with just 10 lines of code, is critical.
· QVAC synergy: If Tether’s QVAC ecosystem eventually defaults to integrating Swarm as its "local AI" cloud memory extension, it will be a direct positive signal.

---

💎 Summary

Tether’s local AI strategy opens a new narrative window for Swarm: from "file storage" to "storing the soul of AI." In the context of AI agents needing permanent, private, verifiable memories, decentralized storage is no longer optional but a core infrastructure.

Currently, BZZ is at a historic low, pricing in its ecosystem stagnation. But if an AI agent ecosystem explodes in the second half of the year (e.g., applications based on ElizaOS or QVAC), the market will revalue Swarm as the "AI data foundation."

Operationally, watch whether Swarm’s official releases dedicated APIs or storage solutions for AI agents, and whether projects adopting Swarm appear in the QVAC ecosystem—these will be signals confirming the bullish outlook.