MasterQuant Responds to Regulatory Consultation on AI-Generated Tokens and On-Chain Model Governance

Global regulators have launched a public consultation on the legal classification, risk boundaries, and governance mechanisms of AI-generated tokens and on-chain model execution. MasterQuant, a leading platform for AI-powered asset management and smart contract automation, has submitted a comprehensive response, advocating for a balanced framework that supports innovation while ensuring compliance and accountability.

1. Background: Regulatory Focus on AI-Native Assets

With the rise of generative AI (AIGC) in Web3, models are now autonomously creating tokens, NFTs, smart contract logic, and even governance proposals. These “AI-native assets” circulate and execute without direct human oversight, raising concerns around legal attribution, algorithmic control, data bias, and explainability.

The consultation paper highlights the need for robust on-chain governance and compliance frameworks to address potential securities implications, systemic risks, and ethical concerns.

2. MasterQuant’s Key Recommendations

MasterQuant’s response outlines five core proposals:

• On-Chain Model Identity Registry: All AI models involved in asset generation should register a unique on-chain identity, including version history, training data sources, and developer credentials.

• Auditable Inference Trails: Each model execution must generate an on-chain record detailing input data, logic path, and output, enabling third-party verification and regulatory audits.

• Token Generation Permission Controls: Platforms should define boundaries for model-generated assets to prevent unauthorized issuance of high-risk tokens or contracts.

• Model Governance via DAO: A “Model Governance DAO” should be established, allowing developers, users, and auditors to collaboratively manage upgrades, parameter tuning, and behavioral corrections.

• Compliance Tagging System: AI-generated assets should carry on-chain compliance tags indicating risk level, audit status, and applicable jurisdictions.

3. Industry Impact and Ecosystem Response

MasterQuant’s proposals have received support from infrastructure projects and legal-tech organizations including Chainlink, OpenAI’s Web3 Lab, and the a16z Crypto Policy Network. Several platforms are already testing model registration and inference audit modules to explore compliant issuance of AI-generated assets.

Experts view this consultation as a turning point for AI asset regulation, shifting the paradigm from “technical freedom” to “responsible governance.”

4. Value for Users and Developers

• Enhanced Trust: On-chain model identity and inference records increase user confidence in AI-generated assets.

• Clear Compliance Pathways: Developers can participate in governance and registration processes, reducing legal exposure.

• Platform Transparency: MasterQuant will offer public access to model execution logs and compliance tag APIs.

• Improved Asset Safety: Permission controls and behavioral correction mechanisms reduce the risk of malicious or unstable asset generation.

5. Roadmap

MasterQuant will roll out the following initiatives over the next six months:

• Model Registry Portal: Enabling identity registration, version control, and compliance tagging for AI models.

• Inference Audit Module: Automatically logs model execution paths for user and auditor access.

• Model Governance DAO Toolkit: Includes voting systems, parameter adjustment interfaces, and behavioral correction modules.

• Compliant Asset Generation Engine: Restricts model output scope and frequency to meet regulatory standards.

MasterQuant will also participate in global standards bodies and policy forums to help shape international frameworks for AI-generated asset governance.