Article | Han Gong Observations
On September 1, 2026, the Beijing Cyberspace Administration announced that 68 key enterprises have achieved mutual recognition of both explicit (visibly labeled) and implicit (machine-readable metadata watermarks) identifiers, with a cumulative total of 597 billion pieces of information properly tagged at the generation stage and over 800 million AI-generated content items displaying verified identifiers at the dissemination stage.
On the first anniversary of its establishment, Li Fei-Fei’s World Labs unveiled Atlas in San Francisco—generating a 1-minute 1440p video from just six photos. Official blind tests revealed that, in specified camera motion comparisons (where Atlas directly reads camera coordinates while competitors rely on text descriptions), Atlas achieved a 94% win rate against Seedance 2.5 (Science and Technology Daily, Tencent News, etc., September 2, 2026).
While instructing text passages to begin with “This article was generated by AI,” it pushes “filling in the back of photos and imagining distant mountains” to cinematic realism. It’s not that the governance approach is wrong—it’s that these rules were designed for the previous generation of AIGC technology, which has already been left half a step behind by world models.
Over the past year, regulations have truly gained teeth. In April, Jianying, Maoxiang App, and Jimeng AI were publicly summoned by the Cyberspace Administration of China for failing to effectively implement labeling requirements; in May, the Chongqing Liangjiang New Area Market Supervision and Administration Bureau issued the city’s first-ever fine for “fully AI-generated fictional commercial scenarios”—an advertisement featuring AI-generated footage of crowds queuing to buy products, with no human actors and no prominent labeling; on September 2, the second phase of the “Clear and Bright: Rectifying AI Application Chaos” campaign reported the removal of over 5.61 million pieces of non-compliant content and the suspension of over 49,000 accounts (China Cyberspace Administration, September 2, 2026).
Paper rules have become real regulation. This year’s regulatory implementation has established the foundational institutional framework for AIGC content provenance.
But real regulation targets the previous generation of AIGC: text-to-text and text-to-image, which at least have explicit labels and C2PA (Content Authenticity Initiative) metadata.
Videos generated by world models like Atlas often suffer from both explicit and implicit watermarks being compromised during subsequent editing and transcoding: explicit watermarks may be diluted by camera movements, while implicit watermarks can be destroyed by re-encoding, leading to failures in both human and automated detection systems.
The more reliance is placed on post-facto penalties, the more only legitimate entities willing to leave traces are constrained; gray production that hides its origins, disseminates without labels, and moves across platforms is nearly untouched by existing penalty systems. Punitive governance has hit a ceiling.
History had already written the script. In 1865, Britain’s Motor Car Act required a person to walk ahead of automobiles waving a red flag, with a speed limit of just 2 miles per hour—imposing horse-drawn carriage-era rules on mechanical power, nearly stifling the British automotive industry. Only after the establishment of license plates, driver’s licenses, and insurance systems did regulation evolve from indiscriminate restrictions to a framework of identifiable accountability and divided responsibility.
Domestic e-commerce went through the same path: early attempts to shut down stores and impose fines couldn't stop scammers from changing their identities; ultimately, real-name verification and credit scoring filtered out trustworthy sellers.
Germany amended its Road Traffic Act in 2017 to allow L3, and in 2021 enacted the Autonomous Driving Act to permit regular L4 operations, also following a three-stage approach: "sandbox first, then standards, followed by trust."
One year since the AIGC label, coinciding with the end of the second phase: the foundation for penalties has been solidified, but penalties alone cannot take us further.
Why can't you reach the end? Because under information asymmetry, punishment can only filter out those who aren't afraid of penalties, not those who are trustworthy.
Today, that “AI-generated” watermark is a symbolic carrier of the digital age. Drawing on Goffman’s theory, creators deliberately adding this标识 are providing a traceable credit endorsement for their content on the platform. From Spencer’s separation equilibrium logic: the goal of institutional design has never been to eliminate forgery, but to make forgery unprofitable.
Technological iteration never stops, and the methodsof fraud and arbitragealsocontinue to evolve—the true anchor of governance has never been technological perfection, but rather the distribution of incentives.
Honest creators accept short-term traffic friction in exchange for long-term traceable records; if batch processing factories dare to label themselves, their origins are immediately exposed; if they dare not label themselves, they automatically fall into the "untraceable suspicious set." Labeling is not a moral appeal, but a rational separation mechanism under game theory.
But this separation mechanism was distorted at the platform’s initial review stage. Regulators handed the first filter to the platform, like high-speed rail security screening: to guard against the extremely low probability of dangerous individuals, all passengers must line up and pass through scanners.
A human author had their product description polished by AI, but the algorithm flagged it as "suspected AI-generated content" and imposed content restrictions—this is a widespread industry pain point: how to define the boundary for labeling when AI polishing and human-AI collaborative creation are involved. When the author appealed, the platform refused to explain its algorithmic logic, citing trade secrets. In the landmark case Beijing Internet Court (2023) Jing 0491 Min Chu No. 16846, user Tang Mou published a purely human-written, spontaneously created text of over two hundred characters, which was mistakenly flagged as AI-generated and subsequently restricted and silenced. The court ultimately ruled that the platform had breached its contract.
Real people are forced to prove they’re not bots by using “imperfect” behavior, while mass plagiarism factories simply rebrand and come back to life. Rules lock down those who follow them, but cannot contain those who don’t.
Worse still, the term “AI-generated” is shifting from a compliance sticker to a signal of low quality. Within this year, Xiaohongshu removed 56,500 AI-faked celebrity posts and 1,372 accounts—those behind the abuse never intended to follow the rules; they only care whether their traffic arbitrage model still works. In the current stage, where platform algorithms have not yet implemented credit stratification and still treat labels as indicators of low quality, honest producers who clearly label their content as AI-generated find their apples unsellable, as consumers automatically assume “low quality” because of the label.
The identifier was meant to narrow down the suspicious set, but now it has become a sieve that unfairly filters out honest users.
The tide is shifting. In its August announcement, Xiaohongshu clearly stated: "Actively identifying AI-generated content or AI virtual personas will not affect traffic; high-quality creators who proactively disclose their identity may have priority access to the platform's new AI tools and features."
Douyin has also moved the AI self-declaration entry to the primary submission page. The label has shifted from a “penalty item” to a “disclaimer” or even a “priority access pass”—this is the real-world implementation of the Spence signaling model: when honest labels begin to accumulate credit scores, creators develop an expectation of long-term redemption. The historical precedent of traffic prioritization for verified e-commerce sellers is being replicated in the AIGC industry.
This is not just a governance narrative, but an industry narrative.
During the era of the automotive Red Flag Act, profits came from driving schools, license plates, and insurance; during the era of internet anonymity, profits came from real-name verification, payment channels, and Sesame Credit.
One year since the AIGC label, there are four potential directions.
Layer 1: Traceability Infrastructure. Watermark SDK and C2PA/GB 45438 issuance gateways have shifted from optional configurations to compliance requirements; medium-sized AI content creators (such as MCNs, corporate marketing departments, and digital publishing institutions) face annual technology upgrade costs of approximately RMB 500,000–1,500,000 to integrate into the labeling pipeline, with additional operational and verification costs.
According to The Business Research Company, the full-chain market for C2PA content provenance solutions has grown from $1.63 billion in 2025 to $2.06 billion in 2026, and is projected to reach $5.12 billion by 2030.
The domestic necessity is the GB 45438-2025 implicit identification and metadata gateway; the C2PA issuance module is currently an added value for outbound content and multinational brands, and domestic players profit from the "dual-track bridging" model.
On the condition that regulatory requirements for gateway modifications continue to tighten.
Layer two: Compliance audit. Nearly a thousand registered generative AI services (as of end-June 2026, the CAC has cumulatively registered 988 and recorded 598) must prove their labeling schemes comply with GB 45438-2025: explicit label size and placement, implicit metadata fields, tamper-resistant signatures, and regulatory verification interfaces—omit any one of these four requirements, and the next inspection could result in traffic throttling or a regulatory meeting.
Currently, Alibaba Cloud, Qimingxingchen, and NetEase Yidun bundle auditing capabilities with watermarking and cloud security as bundled offerings, while the Electric Standard Institute platform conducts self-inspections on the regulatory side—similar to how traffic police initially operated their own vehicle inspection stations in the early days of annual car checks: functional, but with questionable neutrality.
In automotive history, the 2004 Road Traffic Safety Law established the socialization of vehicle inspections, and in 2014, the government was explicitly required to withdraw from operating inspection services, allowing private inspection lines to become a multi-billion-dollar industry.
The turning point for AIGC labeling audits will be similar: when regulators begin to accept compliance reports issued by independent third parties, independent audit institutions (such as Shanghai Automotive Inspection, which has issued labeling reports for ideal and Mercedes-Benz onboard large models) will be separated from bundled services. This funding is “budget born from penalties,” making it cyclical-resistant and highly certain.
Provided that the number of registered entities continues to grow.
Layer three: credit tiering. If identification is viewed as a “content insurance policy,” credit tiering functions like a car insurance premium adjustment mechanism: creators with consistent compliance receive boosted traffic weight and priority placement by brands; accounts that repeatedly miss tagging are subject to traffic restrictions and reduced ranking.
In August, Xiaohongshu stated that "actively labeling does not affect traffic, and high-quality creators will be prioritized for early access to new features." Douyin has moved the statement entry to the forefront—platform-based credit tiering is already emerging.
The true commercial value lies in three areas: first, evidence against false positives. Following the Beijing Internet Court case (2023) Jing 0491 Min Chu No. 16846, human creators wrongly flagged by algorithms require a "Human Creation Certificate" to appeal; the Joint Trust Time Stamp has already been integrated into the AIGC PAS platform, with a baseline certification price of 10 yuan per file and over 120,000 court documents recognizing its validity—this is the first standardized business in the third layer.
Second is platform-specific credit scoring SaaS. Third is a cross-platform credit passport—brands will eventually demand that only influencers with a credit score above 90 be targeted, but data silos between platforms won’t be dismantled in the short term; credit scoring doesn’t need to wait for mutual recognition to begin—Douyin can run its own system, Xiaohongshu can run its own, just like Ping An and PICC each maintain their own claims records, while still offering premium discounts within their respective systems.
Provided that the platform's algorithm no longer penalizes tagged content.
Layer four: Content provenance verification. Labeling is writing; verification is reading—once labels are applied, platform spot checks, regulatory audits, brand due diligence, and ordinary users uploading an image to verify authenticity all rely on a “currency detector”: C2PA Manifest parsing, SynthID/Alibaba invisible watermark extraction, and GCmark national standard code verification.
In the global C2PA content provenance solutions market, Verification and Inspection Applications are the fastest-growing sub-segment. The domestic digital watermarking market is approximately RMB 5.28 billion, with inspection endpoints primarily served by GCmark from the Telecommunication Standards Institute, Alibaba Cloud’s AI Digital Authentication, Qimingchen’s MACCW, and NetEase Yidun; supply of independent third-party inspection SaaS solutions remains limited.
This capability and product anti-counterfeiting share the same origin: the Maotai code itself costs only a few cents; the industrial and commercial verification system and the brand’s risk control backend are where the recurring fees truly lie.
Unlike the first-layer shovel-selling model, verification generates recurring revenue through per-use calls and annual subscriptions, and when metadata is stripped during cross-platform sharing, robust watermark extraction becomes the final gatekeeper—those who reliably extract the source lineage even after five screenshots will be the verification gun manufacturers in AIGC.
The identification method turns "content source" into an auditable asset, allowing those selling shovels to recoup their costs first.
Conditional on breakthroughs in robust watermarking technology and the establishment of user payment habits.
Looking back ten years from now, every piece of content tagged today as “AI-generated” will seem like the first license plate on an early automobile—clumsy, awkward, and drawing curious glances from passersby.
But it is precisely this license plate that gives law-abiding individuals traceable identities and leaves those who break the rules with nowhere to hide. The state builds the roads and sets the direction with its signage; entrepreneurs and investors must calculate which sections of this road can generate toll revenue.
The first anniversary is not an endpoint, but a ticket to a credit-based system. When Li Feifei and others elevate generative technology to the level of world models, the only thing that can keep pace is smarter credit—not harsher punishment.
Those who marked it hold a certificate that others won’t have ten years from now.
Information notice:
This analysis is based on logical deductions from existing policies and industry dynamics; the actual implementation timeline is influenced by multiple variables and does not constitute investment advice.
The one-year data mentioned in this article is sourced from the Beijing Cyberspace Administration’s work update on September 1, 2026, the “Clear and Bright” special campaign update from China Cyberspace Administration on September 2, 2026, the People’s Daily report on April 28, 2026, and the public information released by the Liangjiang New Area Market Supervision and Administration Bureau on May 2026; Li Feifei’s World Labs Atlas product information is derived from reports by the Sci-Tech Daily and Tencent News on September 2, 2026; C2PA Content Provenance Solutions market data (increasing from $1.63 billion in 2025 to $2.06 billion in 2026 and projected to reach $5.12 billion by 2030, with a CAGR of approximately 25.6%) comes from The Business Research Company’s “C2PA Content Provenance Solutions Global Market Report 2026” (published March 2026); the domestic digital watermarking market size of approximately RMB 5.28 billion is based on domestic security and watermarking industry research reports; the Beijing Internet Court case reference is (2023) Jing 0491 Min Chu No. 16846; Xiaohongshu platform rules are from the governance announcement by Shuguanjia on August 7, 2026; Douyin feature adjustments are from the official one-year anniversary announcement on August 31, 2026; filing data is from the National Cyberspace Administration’s announcement as of June 30, 2026 (988 registered, 598 filed); product information for Alibaba Cloud AI Digital Authentication, Qimingchen MACCW, NetEase Yidun, Reilai Wisdom, and Guotou Intelligent Meiya Authenticity is sourced from each vendor’s official product pages; the Joint Trust Timestamp AIGC PAS platform and the base certification price of RMB 10 per file are from tsa.cn and aigc.tsa.cn; the Shanghai Automotive Inspection车载大模型标识 report is from public reports (July 2025); the annual technical transformation cost for mid-sized AI content producers, estimated at RMB 500,000–1.5 million, is inferred from industry testing and vendor quotations; historical references such as the UK’s Motor Car Act and Germany’s Autonomous Driving Law are publicly available legal records; Goffman’s concepts of symbolic carriers and impression management are from “The Presentation of Self in Everyday Life”; Spencer’s separation equilibrium theory is from the Quarterly Journal of Economics.