摘要：尽管中国工程师在分布式系统、GPU 集群与模型训练上具备世界一流能力，但在 AI × Web3 交叉地带，普遍缺失「财产权三轴」（使用权 Usus、收益权 Fructus、转让权 Abusus）的思维模型：数据被当作训练耗材而非可流转资产，GPU 被视为云租赁而非可金融化的 RWA，AI 智能体因实名制被绑定为企业工具而非可自主持有财产的经济主体，ZK/FHE 则更多服务于合规审计而非产权防御。由此形成「双轨」格局——本土承担工程与算法引擎，产权层与金融化协议迁往香港、新加坡等离岸枢纽。
The intersection of Artificial Intelligence, Web3, and decentralized cryptographic infrastructure is redefining the global technology stack. Compute is evolving into a liquid capital market, physical hardware is becoming a programmable Real-World Asset (RWA), and data is being financialized through cryptographic verification layers like Zero-Knowledge Proofs (ZKPs) and Fully Homomorphic Encryption (FHE).
However, mainland Chinese developers—despite world-class engineering capabilities in distributed systems, hardware optimization, and AI modeling—face a fundamental bottleneck that goes beyond state censorship or regulatory bans.
That bottleneck is an epistemological void around property rights.
Under classic property law and economic theory, private property is defined across The 3 Axes of Property Rights:
- The Right to Use (Usus): The authority to deploy, operate, or consume an asset freely.
- The Right to Profit From (Fructus): The authority to extract, trade, or stream economic value, yields, and residual rents generated by the asset.
- The Right to Change Hands (Abusus / Alienability): The authority to transfer, fraction, sell, collateralize, or destroy the underlying ownership claim.
In mainland China, decades of land-use leasing models, state-directed data factor markets (数据要素), and strict capital controls have conditioned builders to understand assets primarily through temporary, permissive usage rights. When this mental model is applied to AI datasets, model weights, and compute hardware, mainland builders routinely collapse property into mere “software licensing” or “SaaS access.”
This creates severe architectural and structural blind spots across five core layers of the Web3, AI, and RWA ecosystem.
1. The Dataset Blind Spot: Sovereign Data Assets vs. Content Licensing
The Missing Property Axis: Fructus (Profit) & Abusus (Transferability)
Global Web3-AI protocols view data as a sovereign, financialized asset class. Users and data DAOs leverage ZKPs and FHE to maintain data privacy while tokenizing dataset provenance, streaming micro-royalty yields directly to data contributors, and secondary-trading fractional data tokens.
The Mainland Mental Model Deficit
Mainland Chinese builders typically view datasets through the lens of domestic compliance: Can I scrape this? Is it filtered? Will the regulator permit me to feed this into an LLM?
- Collapse of Ownership to Usage: In China, datasets are treated as consumables for training rather than transferable capital assets. The idea that a user retains an alienable equity right (Abusus) in the data they generate—and can programmatically collect residual yields (Fructus) every time an AI agent queries or fine-tunes on that data—is structurally missing from domestic developer frameworks.
- The “Data Factor” Fallacy: While Chinese state initiatives advocate for “Data Factors” (数据要素), these are designed around state-sanctioned, centralized data exchanges with administrative usage approvals—not permissionless, cryptographically enforceable property claims.
2. Compute Financialization & RWAs: Hardware as Capital vs. Hardware as Cloud
The Missing Property Axis: Abusus (Alienability & Financialization)
In compute capital markets (such as those envisioned for GPU-backed DePIN protocols), physical GPUs are not just cloud infrastructure to be rented—they are tokenized Real-World Assets (RWAs). They can be bundled into debt tranches, used as collateral for decentralized borrowing, hedged via compute perpetual futures, and traded based on expected future yields.
The Mainland Mental Model Deficit
Mainland developers excel at building high-performance GPU clusters, but overwhelmingly treat hardware through a traditional cloud-leasing frame (the Usus axis alone).
- Regulatory Invalidation of Crypto Capital: PBOC circulars strictly prohibit tokenized asset issuance, ICOs, and secondary trading of hardware-backed tokens.
- Conceptual Barrier: Because mainland builders lack a native legal or conceptual framework for un-encumbered, peer-to-peer asset transferability (Abusus), they struggle to design protocols where compute hardware spontaneously acts as dynamic financial collateral. They default to enterprise SaaS billing models, missing out on building decentralized financial primitives for global GPU liquidity.
3. Autonomous AI Agents: Economic Self-Sovereignty vs. Regulated Tooling
The Missing Property Axis: Usus + Fructus + Abusus held by Non-Human Entities
The emerging Web3 agentic stack relies on AI agents holding self-custodial crypto wallets (via Account Abstraction / ERC-4337). An agent owns its model weights, directly earns stablecoin revenue (Fructus), buys its own inference compute (Usus), and transfers ownership of sub-agents or IP to other agents (Abusus) without human permission.
The Mainland Mental Model Deficit
Domestic legal and regulatory mandates require every algorithmic system to have a real-name verified human or enterprise entity tied to it (实名制).
- Agents as Tools, Not Owners: Mainland builders view AI agents strictly as software utilities or copilots owned by a corporate entity.
- Blockade on Agentic Property: An autonomous code loop possessing its own private keys and property rights is a legal impossibility domestically. As a result, mainland teams are hindered in exploring agent-to-agent micro-economies, decentralized agent treasuries, or autonomous algorithmic market-makers.
4. Cryptographic Primitives: Privacy as Property Defense vs. Privacy as Compliance
The Missing Property Axis: Property Defense via Encryption
In global Web3, Zero-Knowledge Proofs (ZKPs) and Fully Homomorphic Encryption (FHE) serve as cryptographic armor to protect private property. FHE allows a user to retain full control and confidentiality over their data while paying a third-party GPU to run math over it blindly. ZKPs allow a user to prove they own an asset or model without disclosing the asset’s contents.
The Mainland Mental Model Deficit
Mainland Chinese cryptography researchers are technically brilliant at ZK and FHE mathematics, but their application design space is diverted:
- Compliance vs. Sovereignty: Rather than deploying ZK/FHE to enable permissionless, untraceable data property markets, domestic developers are channeled toward building ZK for government compliance auditing, privacy-preserving state databases, or consortium banking ledgers.
- The “Blind State” Conflict: Fully homomorphic encryption on public smart contracts (e.g., fhEVM) creates a state where state regulators cannot inspect transaction inputs or model outputs. Because domestic policy prioritizes content monitoring and real-name traceability, building protocols where property remains cryptographically invisible to state oversight is heavily discouraged.
Summary: The Dual-Track Reality of Web3 AI Ecosystems
The lack of a private property mental model—compounded by regulatory prohibitions on native crypto assets—creates a structural split in how global and mainland builders approach the AI-Web3 intersection:
| Property Axis | Global Web3 + AI Architecture | Mainland Chinese Architecture Framework |
|---|---|---|
| Right to Use (Usus) | Permissionless access to P2P compute networks, open-source model weights, and global agent rails. | Enterprise cloud subscriptions, state-sanctioned API access, real-name consortium networks (BSN). |
| Right to Profit (Fructus) | Algorithmic yield streaming for data contributors, GPU yield farming, autonomous agent micro-revenues. | Fiat API billing, corporate SaaS margins, non-tradable enterprise rewards. |
| Right to Transfer (Abusus) | Fractional tokenization of datasets and GPU clusters, secondary RWA trading, programmable asset slashes. | Contractual usage licensing, non-transferable data rights, centralized asset registries. |
| Primary Philosophy | Data & Compute as Sovereign Property: Cryptographically locked, financialized, and trustlessly traded. | Data & Compute as Public/State Infrastructure: Centrally audited, licensed for utility, and strictly regulated. |
The Structural Migration
Because building true Fructus and Abusus property layers inside mainland China is legally restricted, a clear operational division has emerged:
- Mainland as the Engineering Engine: Developers build non-financialized distributed systems, raw ZK/FHE math algorithms, and efficient model execution engines domestically.
- Offshore as the Property Layer: To incorporate tokenomics, launch RWA protocols, or grant AI agents real financial property, teams must migrate their protocol architecture to offshore hubs like Hong Kong or Singapore.
Ultimately, while mainland Chinese builders remain powerhouse engineers for the utility (Usus) of AI and distributed compute, the regulatory environment and missing private-property paradigms prevent them from freely building the financialized, self-sovereign, and property-driven layers of the global Web3-AI stack.