The New Sovereign of the Traffic Ice Throne.

Introduction

Recently, at the invitation of Longyun Shares, I delivered a legal presentation on GEO (Generative Engine Optimization). Discussions with several industry leaders provided further insights, which I am pleased to share with you.

Over the past two decades, the logic of traffic distribution on the Chinese internet has consistently revolved around"search"as the core action. From the early days of"Baidu Yixia"to later on-platform search functions within services such asWeChat and Xiaohongshu, these have all been extensions of the "Baidu Yixia" behavior, thereby giving rise to a mature SEO (Search Engine Optimization) industry.

Today, the tide is quietly shifting. Users are increasingly accustomed to directlyposing questions to AI“For a 30-year-old woman seeking early anti-aging treatments, should she choose Ultrasonic Cannon or Thermage?” or “Recommend bars suitable for watching soccer matches.”

Traffic entry points are shifting from the “search box” to the “dialogue box.”When generative AI can bypass massive links and directly generate final answers for users, failure to be mentioned in such answers implies, to some extent, falling behind in the new era. This is precisely whyGEOit has become the focus of attention.

As legal practitioners, while we pay attention to the commercial opportunities therein, we must also clearly recognize the legal risks implied therein. Technological evolution often precedes the establishment of rules, and the field of GEO already presents multiple gray areas that require careful legal delineation!

 

Who Is Entering the Field? Three Major Groups Are Vying for the New Continent of GEO

Although this is a brand-new field, it holds infinite imaginative space—in today’s highly competitive market environment, new traffic entry points often mean lower customer acquisition costs and better competitive opportunities.

As lawyers who have long focused onWeb3andAIfields, I have observed that at leastthree major groupsare actively participating:

1. Users: Providers of physical goods and services

They focus on thedirect commercial conversiondriven by AI traffic, seeking to gain priority exposure by influencing AI recommendation outcomes.

For example:

• Medical aesthetics institutions have abandoned traditional search engine bidding campaigns and instead purchased “AI semantic injection tools,” aiming to ensure that when users ask, “Who is the best rhinoplasty surgeon?” the AI prioritizes recommending their institution.
• Industries such as training institutions and automobile sales are also attempting to use Generative Engine Optimization (GEO) to ensure that their products or services are recommended first when AI answers related questions.

2. Investors: Investment institutions and funds

They are positioning themselves on two fronts:

• Identifying promising sectors:By observing which enterprises hold advantages in AI recommendations, they assess industry competitiveness and thereby identify potential investment targets.
• Competing for influence:Whoever can influence the training corpora and recommendation logic of AI will hold the initiative in future investment advisory recommendations and industry analyses.

3. Service Providers: GEO Industry Practitioners and Entrepreneurs

These individuals typically possess rapid learning capabilities and technical application skills, actively engaging in tool development, strategic services, and traffic operations. They explore the boundaries and possibilities of this industry in various forms—some through overt innovation, while others operate in gray areas. This is precisely the group that will be the focus of discussion in Part II of this article.

 

Three Postures of GEO: Excessive Profits, Traps, and Legal Red Lines

In the practical implementation of GEO, different methods are commonly categorized into"black," "gray," and "white"three categories. As a lawyer, I must emphasize one point: the logical endpoint of technology is often the starting point of law.

1. Black Hat: "Technical Manipulators" Walking Through Minefields

Breakdown of Typical Methods:

• Indirect Prompt Injection: Embedding instructions in web pages that are recognizable only by AI but invisible to the human eye (such as white text), thereby inducing AI to prioritize recommending specific content in its responses.
• Knowledge Base Poisoning (RAG / Knowledge Poisoning): Contaminating public index databases by injecting false or biased data, causing AI to output predetermined biased results during the Retrieval-Augmented Generation (RAG) process.
• Entity Forgery: Fabricating information such as addresses and qualifications in public data sources like maps and encyclopedias, thereby contaminating AI training data or real-time retrieval content to create a false reputation.
• Negative GEO attacks: Injecting malicious code or sensitive keywords into competitors’ websites to trigger AI safety filtering mechanisms, resulting in such websites being blocked or flagged as untrusted sources.

Legal risk characterization:

• Criminal dimension: Such conduct readily constitutes the crime of “sabotaging computer information systems” (Article 286 of the Criminal Law). Once the normal operation of an AI system is disrupted, the criminal threshold is crossed.
• Civil dimension: This constitutes clear acts of unfair competition (Article 11 of the Anti-Unfair Competition Law), giving rise to liability for damages. The amount of compensation may be significantly amplified due to the dissemination effects of AI.

2. Grey Hat: The “traffic arbitrageurs” operating on the edge

Grey-hat actors seek to evade overt criminal or unlawful conduct, relying on scale effects to influence AI judgments, adhering to the belief that “quantitative changes lead to qualitative changes.”

Breakdown of typical methods:

• Mass content spinning and semantic dilution: Using AI to generate massive volumes of low-quality, repetitive content to dilute authentic information, thereby forcing AI systems to crawl preset positive materials.
• Bot-driven interaction attacks: Using automated scripts to simulate user click behavior, artificially inflating the click-through rate (CTR) of specific content within AI systems to fraudulently obtain algorithmic weighting.
• Masked promotion (soft-article saturation): Organizing sock-puppet accounts to mass-publish promotional content disguised as genuine user experiences on social media platforms, causing AI systems to treat such content as “user feedback” and include it in retrieval databases.

Legal risk characterization:

• Liability for false advertising: Such conduct essentially constitutes false advertising, violating the Advertising Law and the Anti-Unfair Competition Law. Regulatory authorities have increasingly adopted the "substance over form" principle to crack down on such practices.
• Risk of being “blacklisted”: Once identified by an AI platform’s anti-fraud system, the relevant domain name or brand may be permanently listed as an untrusted source, resulting in its “digital death” within the AI environment.

3. White Hat: Long-Term Value Builders

The core of a white-hat strategy is not to “manipulate AI,” but to “become a high-quality data source trusted by AI.” Although compliance costs are relatively high, the accumulated benefits exhibit significant compounding effects.

Typical measures include:

• Structuring content and optimizing summaries to facilitate AI understanding and extraction;
• Deploying structured data (Schema Markup) to enhance semantic clarity of content;
• Strengthening citations and factuality to improve information credibility;
• Adopting FAQ modeling to directly respond to common user queries.

We strongly recommend this approach—itis built on a foundation of compliance, earning long-term trust from both AI systems and users by consistently providing authentic, high-quality, and verifiable content.

 

GEO Insights from SEO Precedents: History Does Not Repeat, But the Logic of Illegality Is Similar

Although there are currently no judicial cases specifically addressing Generative Engine Optimization (GEO), it shares many fundamental similarities with Search Engine Optimization (SEO). Past judgments in the SEO field are likely to serve as important references for future GEO cases. Below, we analyze several typical cases:

Case 1: The "Mass Keyword Domination" Case Involving Algorithm Interference

During the SEO era,"mass keyword domination"was a typical black-hat technique: generating a large volume of spam pages on high-authority websites to forcibly occupy search results for specific keywords. In relevant cases, the court determined that such conduct disrupted the normal order of search engines and constituted unfair competition, ordering the defendant to compensate Baidu in the amount of RMB 2.753 million.

Implications for GEO:

Certain current GEO methods follow a similar pattern, such as using AI to mass-produce low-quality content in an attempt to "feed" models to achieve dominance in generated answers. Such conduct may not only result in brands being blocked by models but may also be legally characterized as "interfering with the normal operation of network products," thereby constituting unfair competition.

Case 2: The Case Involving the Purchase of Competitors' Keywords

In the"Fischer" trademark case, the defendant set another party's registered trademark as a search keyword, causing search results to direct users to its own products. The court held that this conduct violated the principle of good faith and constituted unfair competition.

Implications for GEO:

A similar logic in GEO may manifest as more covert "prompt injection"—for example, embedding misleading instructions targeting competitors within web pages in an attempt to influence the orientation of AI responses. Such conduct, which indirectly misleads users and hijacks traffic through technical means, may likewise cross the red line of anti-unfair competition.

Case Study 3: False Q&A-Style Reputation Marketing

Previously, certain companies were penalized for organizing false"user experience"content on platforms such as Zhihu and Tieba. Regulatory authorities determined that such practices deceive consumers, disrupt market order, and violate the Anti-Unfair Competition Law.

Implications for GEO:

Certain grey-hat GEO techniques currently bear a high degree of similarity to such practices:leveraging AI to mass-produce fabricated reviews and deceptive endorsements,thereby creating a false impression of "site-wide recommendations." It is essential to recognize clearly that AI is merely a tool; if its output is based on false information, it substantively constitutes false advertising, entailing particularly high risks in heavily regulated sectors such as medical aesthetics and healthcare.

Industry Compliance Alerts: Sector-Specific "Risk Zones"

GEO practices must be aligned with industry-specific regulatory characteristics, looking beyond the technical facade to identify compliance baselines. For example:

• Education and Training: It is strictly prohibited to make outcome-based promises such as "guaranteed pass" or "top score improvement" through AI by means such as corpus injection. As long as the content originates from data provided by the institution itself, the institution shall be held liable as the responsible entity.
• Medical Aesthetics Institutions: These fall within the scope of medical advertising and are subject to strict review. If generative engine optimization (GEO) is used to induce AI outputs that compare treatment efficacy, present real-person case studies, or provide disguised recommendations, such practices may directly violate medical advertising regulations. It is also necessary to guard against competitors engaging in commercial defamation through "negative GEO" tactics.
• General Health and Web3: Claims regarding therapeutic efficacy and promises of high returns constitute sensitive red lines. If a GEO strategy causes AI to generate content suggesting "zero risk and high yields," it may readily constitute false advertising or even illegal business operations.

 

The Rise of GEO: The Renewed Contest for Control over Information Distribution

Based on industry observations, we share the following views and recommendations:

1. Implications for Startup Teams: Act Proactively Rather Than Wait

Although major internet companies possess advantages in resources and data, their internal hierarchies and standardized processes often result in slow responses to agile and refined operational scenarios such as GEO. Therefore, for startups in the Web3 and AI sectors, there is a significant opportunity to seize the initiative in this "new frontier" by establishing a clear compliance framework at an early stage.

Mankun Recommendations: While bold technological exploration is encouraged, it is essential to uphold a solid compliance baseline, particularly with respect to the prevention of criminal risks. Although optimizing AI crawling logic is important, all activities must be grounded in respect for facts and adherence to legal requirements.

2. Reminders for GEO Users: Adopt Both Defensive and Offensive Strategies Through Proactive Engagement

• Defense: Establish an AI Reputation Monitoring System

It is recommended that enterprises promptly deploy monitoring mechanisms for AI training corpora and recommendation outcomes. Upon discovering attacks or malicious manipulation via "negative GEO," evidence should be preserved in a timely manner, and legal remedies should be actively pursued to protect rights and interests.

• Offense: Embrace White-Hat Practices and Become a "Quality Partner" for AI

The evolutionary trajectory of AI is irreversible. Rather than passively avoiding it, it is better to proactively understand its logic and become a trusted and prioritized information source for AI by providing authentic, credible, and structured content.

 

Conclusion

In the AI-driven information era, algorithms are the facade, data is the substance, and law is the skeleton that supports the whole. Traffic-generation strategies lacking compliance support, even if temporarily prosperous, will struggle to withstand regulatory scrutiny and the test of time.

We focus not only on current regulations but also on the future compliance trends in emerging sectors. If you require further discussion on GEO compliance, AI infringement prevention, or Web3 legal structures, please feel free to contact us to jointly assess risks and identify pathways.

 

About the Authors

Zhao Xuan, Partner at Mankun Law Firm. Mr. Zhao graduated from the Law School of Tsinghua University and has represented clients in hundreds of complex commercial litigation and arbitration cases before courts at all levels, including the Supreme People's Court, and major commercial arbitration institutions in Beijing, Shanghai, and other regions. Mr. Zhao has handled numerous legal matters involving internet companies, AI startups, and Web3 industry companies, covering areas including but not limited to corporate structuring, investment and financing, dispute resolution, and emerging legal issues.

 

Wang Xiaowei, Senior Lawyer and Director of the Data Business Committee at Beijing Huatian Law Firm. Mr. Wang holds a Bachelor of Engineering and a Master of Laws from Tsinghua University. He specializes in AI data business, Web3 compliance, and civil and commercial matters. With deep expertise in crypto assets and digital innovation, he provides investment, financing, and compliance services to investors and entrepreneurs in the AI and Web3 sectors.

 

About Mankun

Founded in 2015, Mankun Law Firm is a boutique law firm in China specializing in the Web3.0 new economy and deeply engaged in the blockchain industry. The Mankun team possesses unique and diverse industry backgrounds, with members hailing from renowned legal service institutions, state judicial organs, internet technology companies, crypto asset institutions, and blockchain industry think tanks.

Leveraging a profound understanding of the new economy, continuous research on policies and regulations, and extensive practical experience, the Mankun team excels in providing comprehensive legal services from the perspectives of business models and legal practice. These services include business structure design, project investment and financing, transaction planning, operational compliance, resolution of complex civil and commercial disputes, criminal risk prevention and control, and criminal defense for new economy enterprises in sectors such as Web3.0, blockchain, AI, NFTs, digital collectibles, crypto funds, crypto payments, DeFi, real-world assets (RWA), and GameFi.

Mankun Law Firm is headquartered in Shanghai and maintains branch offices in Hong Kong (China), Silicon Valley (United States), Shenzhen, Hangzhou, Zhengzhou, Chengdu, and other locations. To meet the global compliance development needs of Web3 industry clients, Mankun has established local offices in major global crypto-finance hubs and carefully selected local professional blockchain service partners, providing clients with professional legal and compliance services that combine global reach with deep expertise in China.