A Glimpse of the Future? Why China’s Labeling Law May Signal Global Trends

China's Newest AI Law Might Signal The Way of The Future for the US, UK and Europe

---

Executive Summary

Effective September 1, 2025, China will implement the world's most comprehensive framework for labeling AI-generated content. The new measures—issued by the Cyberspace Administration of China (CAC) and supported by a binding national standard—require all generative AI content distributed publicly in China to include both visible and embedded labels identifying its AI origin.

Service providers must build labeling into their tools by design, retain generation logs, and ensure traceability through metadata and content IDs. Platforms must detect, categorize, and reinforce labels on uploaded content, even if originally unlabeled.

The law introduces a three-tier classification system—confirmed, possible, and suspected AI-generated content—and mandates corresponding labeling and metadata obligations for each. It builds on prior regulations targeting deep synthesis, algorithmic recommendation, and generative AI services.

While uniquely tailored to China's governance model, the law reflects global trends toward transparency, provenance, and platform accountability. For international businesses and law firms, this regulation creates immediate compliance obligations for any AI services or content distributed in China, necessitating technical adjustments to existing products and revised content governance strategies. Legal professionals should prepare clients for potential extraterritorial impacts as similar labeling requirements gain traction across other major jurisdictions.

---

Introduction

When the open-source model DeepSeek-V2 began outperforming Western benchmarks in late 2024, it marked a turning point in global awareness of China’s AI capabilities. For many outside observers, DeepSeek’s emergence was a reminder that China’s generative AI ecosystem—once perceived as lagging behind Silicon Valley—had matured rapidly and was now producing models competitive with the best in the world. But DeepSeek was not an anomaly. It was a product of an increasingly structured and state-supervised AI landscape—one in which regulatory compliance is not optional, and transparency is engineered by design. Among the clearest examples of this shift is China’s new AI labeling law, which takes effect on September 1, 2025.

China’s law introduces the most comprehensive requirements to date for the labeling, traceability, and accountability of AI-generated content anywhere in the world.

China’s AI Labeling Law: What It Is and What It Seeks to Do

On March 14, 2025, the Cyberspace Administration of China (CAC) released a landmark regulatory package composed of the Measures for Labeling Artificial Intelligence-Generated Content and a corresponding mandatory national technical standard, GB 45438-2025. These rules will take effect on September 1, 2025, and together form the most comprehensive generative AI labeling framework in the world to date.

What the Law Requires

The law mandates that all generative AI content publicly distributed in China — including text, images, audio, video, and virtual content — must be labeled both explicitly and implicitly. Specifically:

• Explicit Labels: Content must include visible indicators that clearly inform users it was generated by AI. These may take the form of disclaimers, watermarks, captions, or audio cues, depending on the medium. These labels must be placed within the content file itself, not just the user interface.

• Implicit Labels: Each piece of AI-generated content must also carry embedded metadata or digital watermarks, including:

  • A designation that the content is AI-generated

  • The name or registration code of the service provider

  • A unique content ID that links back to internal logs

• Platforms (such as social media or content-sharing services) are required to detect AI-generated content, categorize it as confirmed, possible, or suspected, and add visible labels and metadata accordingly — even if the original content was unlabeled.

• AI Developers must build labeling into their tools by design, ensure the traceability of every output, and retain logs for at least six months. App stores must screen GenAI tools for labeling compliance before approving distribution.

• App Stores and Hosting Platforms are required to vet GenAI tools for compliance before approving distribution.

What the Law Aims to Achieve

The labeling regime is designed to address a range of policy concerns:

• Preventing misinformation and manipulation: AI-generated deepfakes, fake news, and impersonations can mislead the public, distort political discourse, or cause reputational harm. The law aims to ensure users can recognize synthetic content at the point of engagement.

• Maintaining social order and information integrity: In line with China’s broader platform governance goals, the labeling law is intended to reinforce state control over online narratives, deter the unauthorized use of AI tools, and increase the accountability of both content creators and distributors.

• Enabling enforcement and traceability: By requiring content IDs and persistent metadata, the law allows regulators to trace the origin of problematic content, hold developers and platforms accountable, and investigate violations.

The bottom line is that the law is not just about disclosure—it’s about traceability. If the content is later found to violate laws—such as those prohibiting deepfakes, electoral interference, fraud, or impersonation—the required metadata and content ID allow regulators to trace it back to the provider, user, and time of creation.

The Law’s AI Output Fingerprint

China’s new AI Labeling Rules impose layered obligations on both AI service providers and online content distribution platforms to ensure that AI-generated content is clearly distinguished from human-created content and remains traceable across its lifecycle. This “AI output fingerprint” is achieved through mandatory explicit labeling and embedded metadata, enabling a consistent framework for detection, classification, and attribution.

Obligations for Service Providers — Implement Labeling

Service providers—defined broadly to include developers and operators of generative or synthetic AI services—must implement both explicit and implicit labeling for any AI-generated content made available to users.

These obligations are not optional. Providers must integrate labeling capabilities into their services by design, include appropriate disclosures in their user agreements, and retain generation logs for a minimum of six months. The parallel national standard (GB 45438-2025) provides detailed specifications for implementing these requirements across various media types.

Obligations for Platforms — Detect AI Content

Platforms—such as social media companies, content aggregators, and file-sharing services—must act as enforcement intermediaries by identifying AI-generated content and reinforcing labeling requirements before distribution. They are responsible for scanning uploads, applying classification labels, and embedding metadata to support long-term traceability.

Content must be classified into one of three categories—confirmed, possible, or suspected—and labeled accordingly. In all cases, platforms must attach a label and add their own metadata, including the content’s classification, platform name, and a unique ID.

Categorization and Traceability Framework

The three-tiered classification model that enables platforms to categorize content by certainty of AI origin consists of:

1. Confirmed AI-Generated Content: Implicit metadata is present and validated.

2. Possible AI-Generated Content: No metadata, but user self-identifies the content as AI-generated.

3. Suspected AI-Generated Content: No metadata or self-disclosure, but other indicators point to AI origin.

This classification framework ensures consistent identification of AI-generated content, even when original labeling is absent. It also supports regulatory enforcement by enabling a forensic trail linking content to its source.

---

Note: In all cases, providers must also include implicit labeling—such as metadata, provider ID, and content ID—embedded within the file per GB 45438-2025.

Regulatory Context

This law does not stand alone. It builds on a growing body of interlocking Chinese regulations on AI and digital content:

• The Deep Synthesis Regulation (2023) required providers of AI tools capable of manipulating audio, video, or images to label synthetic media and verify user identity.

• The Generative AI Interim Measures (2023) imposed obligations on providers of GenAI services to prevent discriminatory content, protect user data, and ensure alignment with “core socialist values.”

• The Algorithm Recommendation Rules (2022) required platforms to disclose how algorithmic content is selected and to allow users to opt out of personalized recommendations.

• The Cybersecurity Law (2017) and Data Security Law (2021) established the foundations for algorithm registration, data localization, and government oversight of digital services.

Together, these measures form a cohesive strategy: AI technologies must operate within a controlled, auditable, and state-supervised information environment.

Enforcement and Penalties

While the AI Labeling Measures themselves do not specify detailed penalties, enforcement authority resides primarily with the Cyberspace Administration of China (CAC), with supporting roles from the Ministry of Industry and Information Technology (MIIT), the Ministry of Public Security (MPS), and the National Radio and Television Administration (NRTA). These agencies may impose penalties under broader regulatory frameworks, including the Cybersecurity Law (2017); Data Security Law (2021); and Deep Synthesis Regulation (2023).

Penalties may include:

• Service suspension or content takedown orders, as authorized under Articles 68–70 of the Cybersecurity Law;

• Administrative fines, particularly where violations overlap with unlawful content production, misinformation, or data security failures under the Data Security Law and Deep Synthesis Regulation;

• Inclusion in CAC's “Qinglang” enforcement campaigns, which target online misinformation, content moderation failures, and platform non-compliance (see CAC’s 2025 announcement) (Chinese);

• App store delisting or licensing revocation, as outlined in Articles 10–12 of the Generative AI Interim Measures (2023), requires stores to verify GenAI tools are labeling-compliant before release.

The enforcement model is explicitly platform-centered: content platforms must detect AI-generated content, classify it as confirmed, possible, or suspected, and ensure appropriate labels and metadata are applied, as described in Articles 6 and 7 of the Labeling Measures.

The Measures also mandate metadata and traceability elements—including provider name and content ID—in Article 5 of the Labeling Measures and in the GB 45438-2025 national standard, Section 4 (Chinese). These provisions enable regulators to trace content back to its point of origin—by provider, generation time, and even user or device—supporting administrative enforcement, platform penalties, or potential criminal investigation where applicable.

Why China’s Labeling Law May Signal Global Trends

While China’s AI regulations reflect a unique political and governance context, its new labeling requirements may offer an early blueprint for how other jurisdictions — including the United States and the European Union — will eventually regulate generative AI.

At its core, China’s law responds to the same policy concerns driving Western legislative efforts: the spread of misinformation, deepfakes, identity deception, and the erosion of public trust in digital content. Its solution — mandatory, dual-layer labeling of AI-generated content — is technically ambitious and operationally prescriptive. Yet it aligns with growing international momentum around provenance, traceability, and AI transparency.

The EU AI Act, for example, mandates disclosure when users interact with generative AI systems and requires watermarking or labeling of synthetic audio, video, and images in high-risk use cases. In the U.S., the Biden Administration’s 2023 Executive Order on AI directed agencies to develop standards for content authentication, and the FTC has signaled interest in pursuing deceptive or unlabeled AI content under its consumer protection authority. The Trump Administration’s most recent Executive Order on AI, however, withdrew the previous administration’s AI executive orders. Multiple state bills — particularly in California, New York, and Washington — propose AI labeling in political ads, education, and healthcare.

What distinguishes China’s regime is the degree of specificity and centralization. It goes beyond disclosure at the point of use, requiring technical integration of provenance mechanisms — like embedded metadata and content IDs — directly into AI tools. This “labeling by design” principle may prove attractive to other governments seeking not only transparency but also accountability and enforceability at scale.

The law’s reach is not limited to domestic companies. Any AI-generated content made publicly available within China—regardless of where the provider is based—is required to comply with these labeling requirements. This means that foreign companies offering generative AI services, publishing AI-generated media, or distributing AI tools accessible to Chinese users may fall within the scope of enforcement. App stores and hosting platforms operating in China are also responsible for screening and enforcing compliance, further extending the regulation’s practical reach. Because China is actively pursuing global leadership in AI and technical infrastructure over the U.S., this law might serve as an effective ground for prohibiting non-compliant content created in countries other than China.

If large platforms and developers begin to standardize labeling features globally — due to Chinese law, EU requirements, or reputational pressure — it could become functionally difficult to avoid implementing similar mechanisms in U.S. markets. Voluntary compliance may give way to regulatory convergence, especially in high-impact domains like elections, national security, and legal or medical advice.

In short, China’s AI labeling law should not be dismissed as an outlier. It may be an early signal of a broader global shift toward embedding traceability and authenticity into the architecture of generative AI systems.

Limitations and Evasion Risks

Despite the broad scope and technical precision of China’s AI labeling regime, the law is not immune to circumvention. Developers operating within China can still build or adapt generative AI tools — especially from open-source models like DeepSeek, ChatGLM, or LLaMA — that do not implement required labeling features. By self-hosting or modifying these models, it is technically simple to generate content without either explicit or implicit labels.

Such conduct, however, is clearly unlawful under the Measures. Any provider offering generative AI services to the public in China must:

• File its algorithm with authorities,

• Undergo a security assessment,

• And embed labeling “by design” into its tools.

Likewise, any online platform or app store must verify that AI tools include compliant labeling mechanisms before allowing distribution. Any AI-generated content shared publicly—including images, text, audio, or video—must carry both visible and embedded markers, or risk removal and enforcement.

In practice, enforcement remains the system’s critical bottleneck. While large commercial providers and mainstream platforms will likely comply, smaller developers, academic projects, or gray-market services may evade detection, at least initially. The government appears to be relying on a platform-centered enforcement model, where content without labels will be flagged, restricted, or traced back — incentivizing compliance at scale even if upstream violations occur.

In short, China’s labeling mandate is legally comprehensive, but practically dependent on platform cooperation, app store screening, and regulatory enforcement. As with many regulatory systems, its effectiveness will rest less on perfect compliance and more on deterrence, visibility, and control over distribution channels.

Conclusion

China’s AI labeling law is more than a regulatory milestone—it’s a signal of where the global conversation on generative AI is headed. By embedding traceability into the architecture of AI content, China is not just labeling the output—it’s asserting control over the digital narrative. For lawyers, technologists, and policymakers alike, the message is clear: the era of anonymous AI content is ending, and the future of AI will be as much about governance as it is about innovation.

© 2025 Amy Swaner. All Rights Reserved. May use with attribution and link to article.