By | Han Gong's Observation
September 1, 2026: The Beijing Cyberspace Administration reported that 68 key enterprises have completed mutual recognition of both explicit (visible to the naked eye) and implicit (machine-readable metadata watermark) dual labeling, with a cumulative 597 billion pieces of information from the generation side being properly labeled, and over 800 million pieces of AI-generated content on the distribution side "carrying labels and displaying codes."
On the first anniversary of the regulation, Li Fei-Fei's World Labs launched Atlas in San Francisco—generating 1-minute 1440p videos from just 6 photos. Official blind tests disclosed that in specified camera motion comparisons (Atlas directly reads camera coordinates, while competitors rely on text descriptions of motion), Atlas achieved a win rate as high as 94% against Seedance 2.5 (Science and Technology Innovation Board Daily, Tencent News, etc., 2026.9.2).
On one hand, there's an effort to teach text paragraphs to begin with "This article was generated by AI." On the other, they're pushing "photo completion of the back, brain-completed distant mountains" to cinematic-level realism. It's not that the governance approach is wrong; it's that this set of rules was aimed at the previous generation of AIGC technology, which has already fallen half a step behind world models.
Over the past year, the rules have indeed grown teeth. In April, apps like Jianying, Maoxiang App, and Jimeng AI were publicly summoned for talks by the Central Cyberspace Affairs Commission for failing to effectively implement labeling regulations. In May, the Chongqing Liangjiang New Area Market Supervision Bureau issued the city's first penalty for "entirely AI-fabricated commercial scenarios" to a new energy vehicle company—the advertisement featured no real actors, with AI-generated scenes of people queuing to buy and no significant labeling. On September 2, the second phase of the "Clear and Bright · Rectifying AI Application Chaos" campaign reported the cleanup of over 5.61 million pieces of non-compliant information and the punishment of over 49,000 accounts (China Cyberspace Administration, 2026.9.2).
Paper rules have turned into real regulation. The regulatory implementation this year has established the foundational institutional base for tracing the origin of AIGC content.
However, this real regulation is chasing the previous generation of AIGC: text-to-text, text-to-image, which at least had explicit labels and C2PA (global content provenance standard) metadata.
Videos generated by world models like Atlas face the challenge of dual recognition failure—both by the human eye and detectors—as explicit watermarks can be diluted by camera motion during subsequent secondary editing or transcoding, and implicit watermarks can also be damaged by re-encoding.
The more reliant on ex-post punishment, the more it only binds legitimate entities willing to leave traces; grey market capacity generated anonymously, distributed without labels, and circulating across platforms is almost unaffected by the current penalty system. Punishment-based governance has hit a ceiling.
History has already set the script. In 1865, the UK's "Locomotive Acts" required a man with a red flag to walk in front of cars, limiting speed to 2 mph—applying horse-and-buggy era rules to mechanical power, nearly strangling the British automotive industry. It wasn't until systems like license plates, driver's licenses, and insurance took shape that regulation upgraded from "indiscriminate restriction" to "traceable identity, divisible responsibility."
Domestic e-commerce traveled the same path: early platform bans and fines couldn't stop scammers from switching accounts; eventually, real-name authentication and credit scoring filtered out trustworthy sellers.
Germany's amendment of the "Road Traffic Act" in 2017 to permit L3, and the "Autonomous Driving Act" in 2021 allowing regular L4 operations, also followed a three-stage process: "first sandbox, then standards, later credit."
The first anniversary of AIGC labeling sits at the tail end of the second stage: the punishment foundation is solid, but relying solely on fines cannot go the distance.
Why can't it go the distance? Because in governance under information asymmetry, punishment can only filter out "those not afraid of punishment," not "trustworthy people."
Today, that "AI-generated" watermark is the symbolic carrier in the digital age. Borrowing from Goffman's theory, creators actively adding a label are providing a traceable credit endorsement for their content on the platform. Applying Spence's theory of separating equilibrium: the goal of institutional design is never to eliminate forgery, but to make forgery unprofitable.
Technological iteration won't stop, and the means for forgery and arbitrage will also continuously evolve—the true anchor point for governance has never been technical perfection, but benefit distribution.
Honest creators bear short-term friction in traffic in exchange for long-term traceable records; mass processing factories, if they dare to label, expose their source directly; if they dare not, they automatically fall into the "unverified suspicious set." Labeling is not a moral appeal; it's a separating mechanism under game theory.
But this separating mechanism becomes distorted at the platform's preliminary review layer. Regulators handing the first screening filter to platforms is like high-speed rail security checks: to prevent a minuscule probability of dangerous individuals, all passengers queue up to go through machines.
A real author using AI to polish a product description gets flagged as "suspected AI-generated" by the algorithm and is directly throttled—this is a widespread pain point in the industry: how to define the boundaries of labeling for AI-polishing or human-machine hybrid creation. The author appeals, and the platform refuses to explain the algorithm logic citing trade secrets. In the Beijing Internet Court's typical case (2023) Jing 0491 Min Chu 16846, user Tang ×× posted over two hundred words of pure, real-time human creation, was mistakenly judged as AI-generated content, and faced throttling and silence. The court ultimately found the platform in breach of contract.
Real people are forced to prove they are not machines by being "imperfect," while batch content-scraping factories can be reborn with a new account name. The rules lock down those who follow them, but cannot lock down those who don't.
More troublesome, the phrase "AI-generated" is sliding from a compliance sticker to a signal of low quality. Xiaohongshu disposed of 56,500 AI-simulated celebrity notes and 1,372 accounts within the year—malicious actors never intended to follow the rules; they only care if their traffic arbitrage model fails. In the stage where platform algorithms haven't yet implemented credit layering and still treat the label as a low-quality signal, an honest fruit farmer labeling their content might find their apples don't sell because consumers pre-assume "low quality" due to the label.
Labeling should shrink the suspicious set; now it's become a sieve that inversely eliminates honest people.
The wind is shifting. Xiaohongshu's August announcement clearly stated: "Actively labeling AI-generated content or AI virtual identities will not affect traffic; high-quality creators who take the lead in disclosing their identity may have priority access to the platform's new AI tools and features."
Douyin also moved its AI self-declaration entry point to the primary posting page. The label is turning from a "deduction point" into an "exemption card" or even a "priority experience voucher"—this is the real-world application of Spence's signaling model: when honest labeling starts accumulating credit points, creators gain the expectation of redeeming a long-term deposit certificate. The history of verified e-commerce sellers receiving traffic prioritization is being replicated in the AIGC industry.
This is not just a governance narrative; it's an industrial narrative.
During the automotive Red Flag Act period, profits went to driving schools, license plates, and insurance; during the internet anonymity period, profits went to real-name authentication, payment gateways, and Sesame Credit.
On the first anniversary of AIGC labeling, there are four layers of potential direction.
First Layer: Provenance Infrastructure. Watermarking SDKs and C2PA/GB 45438 issuance gateways transition from optional configurations to compliance necessities. Mid-sized AI content producers (MCNs, corporate marketing departments, digital publishing institutions, etc.) face annual technical transformation costs of approximately 500,000 to 1.5 million yuan to integrate labeling pipelines, with additional costs for operation, maintenance, and verification.
According to The Business Research Company estimates, the full-chain market for C2PA content provenance solutions has grown from $1.63 billion in 2025 to $2.06 billion in 2026, projected to reach $5.12 billion by 2030.
Domestically, the urgent need is for GB 45438-2025 implicit labeling and metadata gateways. C2PA issuance modules are currently value-added items for overseas content and multinational brands; domestic players profit from "dual-track bridging."
The prerequisite is continued regulatory tightening on gateway transformation requirements.
Second Layer: Compliance Auditing. Nearly a thousand registered generative AI services (as of end-June 2026, the National Cyberspace Administration had recorded 988 filings and 598 registrations) must all self-certify that their labeling schemes comply with GB 45438-2025: explicit position and font size, implicit metadata fields, tamper-resistant signatures, and regulatory verification interfaces—failure on any of these four lines means throttling or summons in the next spot check.
Currently, Alibaba Cloud, Venustech, and NetEase Yidun bundle auditing capabilities within watermarking and cloud security products for cross-selling. Platforms like the China Electronics Standardization Institute handle self-inspection on the regulatory side—similar to early police-run vehicle inspection stations in automotive history; they work, but neutrality is questionable.
Automotive history: the 2004 "Road Traffic Safety Law" established socialization of annual inspections; the 2014 order mandated government withdrawal from inspection operations, turning private inspection lines into a multi-billion-yuan market.
The inflection point for AIGC labeling auditing will be similar: when regulators start accepting compliance reports issued by independent third parties, independent audit institutions (similar to the role Shanghai Automotive Inspection Center already plays in issuing labeling reports for NIO, Mercedes-Benz in-vehicle large models) will be peeled off from bundled sales. This money is "budget forced out by penalties," anti-cyclical and highly certain.
The prerequisite is sustained growth in the number of registered entities.
Third Layer: Credit Layering. If labeling is viewed as a "content insurance policy," credit layering is the floating premium mechanism in auto insurance: long-term compliant labelers get increased traffic weighting and priority from brand advertisers; accounts repeatedly failing to label face throttling and demotion.
Xiaohongshu's August statement already indicated "active labeling does not affect traffic; high-quality creators get priority access to new features." Douyin moved its declaration entry forward—credit layering within platforms is already sprouting.
The real commercial value lies in three places: First, evidence against mistaken punishment. Following the Beijing Internet Court case (2023) Jing 0491 Min Chu 16846, real creators mistakenly flagged by algorithms need "human creation credentials" to appeal. United Trust Timestamp has already launched the AIGC PAS platform, with single-file authentication starting at a base price of 10 yuan, accepted in over 120,000 judicial documents. This is the first standardized business within the third layer.
Second, SaaS for intra-platform credit layering. Third, cross-platform credit passports. Brand advertisers will eventually demand "only partner with influencers with credit scores >90," but cross-platform data walls won't be broken down in the short term. Credit layering needn't wait for mutual recognition to begin. Douyin can build its system, Xiaohongshu its own, just as Ping An and PICC each maintain their own claim records; premium discounts within their respective systems operate normally.
The prerequisite is that platform algorithms stop punishing labeled content.
Fourth Layer: Content Provenance Verification. Labeling is writing; verification is reading—after labels are applied, platform spot checks, regulatory reviews, brand due diligence, and ordinary users wanting to know if an image is real, all rely on "currency validators": C2PA Manifest parsing, SynthID/Alibaba dark watermark extraction, GCmark national standard code segment verification.
In the global C2PA content provenance solutions market, Verification and Inspection Applications is the fastest-growing sub-sector. The domestic digital watermarking market is approximately 5.28 billion yuan, with the verification end primarily handled by the China Electronics Standardization Institute's GCmark, Alibaba Cloud AI Intellectual Property Authentication, Venustech's MACCW, and NetEase Yidun. Independent third-party verification SaaS supply remains thin.
This capability shares origins with product anti-counterfeiting: the Moutai code itself costs a few cents, but the industrial and commercial verification system and brand risk control backends are the recurring revenue drivers.
Unlike the first layer selling shovels, verification is a recurring, subscription-based income stream, charged per call or annually. Moreover, when metadata is stripped during cross-platform forwarding, robust watermark extraction becomes the last gatekeeper—whoever reliably masters "reading the source level even after five screenshots" becomes the verification gun manufacturer in the AIGC world.
The labeling measures have turned "content source" into an auditable asset; those selling the shovels recoup their investment first.
The prerequisites are breakthroughs in robust watermarking technology and the formation of user payment habits.
Looking back in ten years, every piece of content labeled "AI-generated" today will be like the first license plate on an early car—inconvenient,突兀, drawing sideways glances from passersby.
But it is precisely this plate that gives rule-followers a traceable identity and leaves no place to hide for rule-breakers. The state builds the road; the labeling measures set the direction. Entrepreneurs and investors should calculate which segments of this road can collect tolls.
The first anniversary is not the end; it's a ticket to the credit passage system. When Li Fei-Fei and others push generation technology to the heights of world models, the only thing that can keep pace is not stricter punishment, but smarter credit.
Those who have labeled hold a deposit certificate that others won't have in ten years.
Information Note:
This analysis is based on logical deduction from existing policies and industry dynamics. Actual implementation pace is subject to multiple variables and does not constitute investment advice.
First-anniversary data in this article are from the Beijing Cyberspace Administration's work report on September 1, 2026; the China Cyberspace Administration's "Clear and Bright" campaign report on September 2, 2026; People's Daily report on April 28, 2026; and publicly available information from the Liangjiang New Area Market Supervision Bureau in May 2026. Information on Li Fei-Fei's World Labs Atlas product is from the Science and Technology Innovation Board Daily, Tencent News, etc., on September 2, 2026. C2PA content provenance solutions market data ($1.63B→$2.06B→$5.12B by 2030, ~25.6% CAGR) is from The Business Research Company's "C2PA Content Provenance Solutions Global Market Report 2026" (published March 2026). The domestic digital watermarking market size (~5.28B yuan) is from domestic security and watermarking industry research reports. The Beijing Internet Court case is (2023) Jing 0491 Min Chu 16846. Xiaohongshu platform rules are from the Shuguanjia governance announcement on August 7, 2026. Douyin feature adjustments are from its official first-anniversary announcement on August 31, 2026. Filing data is from the National Cyberspace Administration's announcement as of June 30, 2026 (988 filings, 598 registrations). Product information for Alibaba Cloud AI Intellectual Property Authentication, Venustech MACCW, NetEase Yidun, RealAI, Guotou Intelligent Meiya Authentication, etc., is from each vendor's public product pages. Information on United Trust Timestamp's AIGC PAS platform and the single-file authentication base price starting at 10 yuan is from tsa.cn and aigc.tsa.cn. Shanghai Automotive Inspection Center's in-vehicle large model labeling report is from public reports (July 2025). The annual technical transformation cost range of 500,000–1.5 million yuan for mid-sized AI content producers is inferred from industry practice and vendor quote ranges. Historical cases such as the UK's "Locomotive Acts" and Germany's "Autonomous Driving Act" are publicly available legal history. Goffman's concepts of symbolic carriers and impression management are from "The Presentation of Self in Everyday Life." Spence's theory of separating equilibrium is from "Quarterly Journal of Economics."
