Generative Artificial Intelligence (GAI), while driving industrial transformation and innovation, has introduced unprecedented governance challenges including deepfakes, algorithmic bias, and provenance gaps. In response, China’s Labeling Method for Content Generated by Artificial Intelligence establishes a dual-track regulatory system combining explicit visual identifiers and tamper-resistant implicit watermarks embedding metadata like timestamps and data trails. This framework aims to create an end-to-end oversight chain—embedding identifiers at generation, enabling verification during dissemination, and ensuring traceability upon consumption—adapting copyright law’s publicity principles to shift from "signature presumption" to "technological determination" for AI-generated content. However, implementation faces critical barriers: obligations are structurally misaligned across stakeholders, with model developers lacking mandates to architect foundational watermarking interfaces, service platforms overburdened by metadata validation duties that strain SMEs and end-users operating without risk-tiered responsibilities. Further complicating enforcement, fragmented metadata formats, proprietary/open-standard conflicts, and outdated national standards create interoperability voids that hinder cross-platform verification, particularly as emerging technologies like latent-space video compression outpace regulatory updates. To address these dual challenges of obligation imbalance and technical fragmentation, a tiered governance approach is essential. Upstream, developers must be mandated to preset multimodal implicit labeling interfaces and deploy automated integrity checks, establishing traceability at the architectural source. Midstream obligations should align with platform capabilities, focusing on reliable visible tagging and “notice-and-takedown” mechanisms akin to e-commerce governance, avoiding undue technical burdens. Downstream, user duties require differentiation—minimal labeling for general content creation versus stringent metadata verification for professional outputs like legal analyses, coupled with substantive review for identifier-removal requests to prevent abuse. Concurrently, metadata standardization demands dynamic co-regulation: unifying cross-modal identifiers through universal “AI_Content_ID” fields anchored to blockchain-certified provenance records; establishing agile standard-revision protocols using regulatory sandboxes and unannounced inspections to keep pace with innovations like Parquet columnar storage; and legally recognizing compliant metadata systems as copyright-enforceable “technological measures” to close enforcement gaps. Future policy must prioritize inter-departmental coordination for cross-sectoral standard harmonization, risk-calibrated regulatory pilots in sensitive domains like healthcare, and proactive engagement in global forums such as ISO/IEC JTC1—collectively advancing a governance paradigm that balances innovation incentives with robust risk mitigation, steering GAI from disruptive growth toward responsible deployment.