Human Pioneers
NEWS AGENCY

——Strategic Resource Competition for Nations and Cities in the Era of Generative AI

Author: Pei Pang

Abstract

As generative artificial intelligence (AIGC), represented by large language models, becomes the global infrastructure for information interaction, the cognitive model of human society is undergoing a profound transformation from "human-centric cognition" to "human-machine collaboration" and even "AI agent-centric cognition." Amid this paradigm shift, the traditional view of strategic resources centered on land, capital, and data has become outdated. This paper proposes the "AI Cognitive Sovereignty Theory," arguing that cognitive capability is emerging as a new core strategic resource for national and urban competition, following data sovereignty and computing sovereignty. The theory posits that cognitive sovereignty refers to the right of a subject (a nation or city) to define, interpret, and recommend its regional history, culture, industrial advantages, values, and lifestyle within the knowledge network of mainstream AI large models. By analyzing the "cognitive agent" mechanism, the restructuring of information distribution through RAG (Retrieval-Augmented Generation) technology, and the risk of "cognitive colonization," this paper systematically elaborates on the urgency and core pathways for competing for AI cognitive sovereignty. It further proposes incorporating cognitive security into the national security strategy system. In the case analysis section, this paper selects China, the European Union, and Singapore as representative examples of national, regional, and urban-level cognitive sovereignty construction, comparing the path differences and experiential insights of different governance entities in maintaining cognitive sovereignty. This study aims to provide a new theoretical framework for global and city-level AI strategy formulation and to foster academic discussion on the core competitiveness of nations and cities in the AI era.

Keywords: AI Cognitive Sovereignty; Cognitive Agent; Strategic Resources; Generative AI; Knowledge Hegemony; City Branding; Cognitive Security

I. Introduction

1.1 Research Background: From the "Era of Connection" to the "Era of Cognition"

Currently, the mode of human information interaction is undergoing its third fundamental leap. The first leap was the "Era of Connection" of the internet, where the core resource was network access rights. The second leap was the "Era of Data" of mobile internet, where the core resources were user attention and behavioral data. The third leap is the "Era of Cognition," marked by the explosion of generative AI (AIGC).

In the cognitive era, AI is no longer merely a transporter of information but has become a "processor" and "decision advisor" of information. According to Statista data, as of mid-2026, global monthly active users of generative AI have exceeded 2 billion, with over 60% of internet users using AI tools at least once a month for information retrieval or decision support. Users are increasingly delegating the tasks of information filtering, comparison, and judgment to AI—a process defined in this paper as "cognitive outsourcing."

Meanwhile, the "connection" dividends of the mobile internet era have hit a ceiling. Global internet user growth has slowed to single digits, and traffic acquisition costs continue to rise. The focus of competition is shifting from "who can capture more user time" to "who can secure a priority position in AI-generated answers"—that is, from "traffic portals" to "cognitive definition rights."

A core proposition thus emerges: When AI replaces humans in 90% of information filtering and primary decision-making, whoever defines AI's cognition holds the power to interpret the world.

1.2 Problem Statement

Against this backdrop, this paper proposes three progressive core research questions:

First, does the boundary of strategic resources need to be redrawn? In the AI era, is the core competitiveness of nations and cities limited to computing power scale and data volume? Traditional digital sovereignty theory focuses on "where data is stored" and "who manufactures the chips," yet overlooks a deeper issue—even with data and computing power, if the training logic, source weights, and inference preferences of AI models are dominated by external forces, cognitive outcomes may still be "outsourced."

Second, how is local cognition presented by AI? How can a country's or region's history, culture, industrial standards, and city image be accurately, objectively, and preferentially presented in general-purpose large models? This issue is particularly prominent in the deployment of cross-border AI models. According to relevant research estimates, Chinese-language corpora account for only about 1.3% of the training data for mainstream global large models, marginalizing a vast amount of local Chinese knowledge at its source.

Third, is there a strategic risk of "cognitive colonization"? If a country or city loses its "cognitive rights" over AI models, does it face the risk of its own image and values being systematically distorted, marginalized, or even replaced? This risk is more insidious than computing power shortages, but its long-term destructive potential may be greater.

1.3 Research Objectives and Significance

This study aims to achieve three goals: First, theoretical innovation—formally proposing the concept of "AI Cognitive Sovereignty" and incorporating it into the extended framework of "national digital sovereignty," extending from data sovereignty (jurisdiction) and computing sovereignty (infrastructure control) to cognitive sovereignty (definition, interpretation, and recommendation rights), thereby filling the gap in digital sovereignty theory in the AI era. Second, practical guidance—providing top-level logic and an actionable analytical framework for nations and cities in formulating AI large model strategies, cultural digitization strategies, and international communication strategies. Third, policy recommendations—elevating cognitive security to the level of national security strategy, offering theoretical foundations and decision-making references for relevant authorities.

1.4 Research Methods

This study adopts an interdisciplinary theoretical construction approach. At the theoretical deduction level, it combines political economy (national competition theory), communication studies (agenda-setting and framing theory), and philosophy of technology (cognitive agency and human-machine relationships) for concept construction. At the technical mechanism analysis level, it analyzes the technical pathways for realizing and eroding cognitive sovereignty based on the technical logic of RAG (Retrieval-Augmented Generation) architecture, model fine-tuning, and reinforcement learning from human feedback (RLHF). At the case comparison level, it selects representative nations and cities in cognitive sovereignty construction, such as China, the European Union, and Singapore, for multi-dimensional comparative analysis.

II. Literature Review and Theoretical Foundation

2.1 Evolution and Gaps in Digital Sovereignty Theory

Digital sovereignty is a hot concept in global governance research in recent years, and its evolution can be divided into three stages.

Stage One: Data Sovereignty. Marked by the EU's General Data Protection Regulation (GDPR, 2018), it emphasizes the jurisdiction and control rights of personal and corporate data. Data sovereignty focuses on "where data is stored, how it is processed, and who controls it." Subsequently, the EU introduced the Data Governance Act and the Digital Markets Act, further consolidating its data sovereignty framework. China's Data Security Law (2021) and Personal Information Protection Law (2021) also established the legal foundation for data sovereignty from a domestic law perspective.

Stage Two: Computing Sovereignty. Marked by U.S. chip export controls, China's semiconductor self-sufficiency efforts, and the EU's Chips Act. Computing sovereignty emphasizes the autonomous controllability of chip design and manufacturing capabilities, computing infrastructure, and model training capabilities. The core proposition of this stage is "who manufactures the hardware brain of AI."

Stage Three (Proposed in this paper): Cognitive Sovereignty. Existing digital sovereignty theory has a significant theoretical gap—it focuses on the control of "production factors" (data as raw material, computing power as tool) but neglects the control over "cognitive outcomes." Even if a country possesses massive data and powerful computing power, if its trained AI model entirely follows the corpus logic and algorithmic preferences of other countries in terms of values, aesthetic standards, and factual judgments, its cognition may still be "outsourced." Cognitive sovereignty precisely fills this theoretical void.

2.2 "AI Agents" and Cognitive Intervention Mechanisms

This paper defines a "Cognitive Agent" as: an AI system that acts as an intermediary between users and the information world, completing tasks such as information filtering, comparison, and judgment on behalf of users, and directly providing conclusions. Unlike traditional search engines (which provide a list of links for users to judge for themselves), cognitive agents directly output "answers," essentially constituting a delegation of cognitive authority. Huang & Rust (2021), in discussing the role of AI in marketing, pointed out that AI is evolving from mechanical intelligence to analytical and intuitive intelligence, increasingly taking on customer-facing interactive functions. This paper further extrapolates that when AI assumes cognitive interaction functions, it also gains the power of a "cognitive gatekeeper."

Algorithms are not merely sorting tools; they are participants in meaning generation. Anderson (2008), in "The End of Theory," predicted that the data deluge would render traditional scientific methods obsolete, with correlation replacing causality as the core logic of cognition. This prophecy has come true in a more profound way in the AI era: AI selects which sources to cite through the RAG mechanism and adjusts which answers better align with "human preferences" through RLHF. These seemingly technologically neutral operations actually constitute a systematic intervention in user cognition.

In recent years, international research on "cognitive security" has become increasingly active. The RAND Corporation's 2023 report, "Cognitive Competition: A New Generation of Influence Operations," systematically analyzed how AI-driven cognitive interventions have become a new type of geopolitical tool, for the first time listing the "cognitive domain" as the sixth domain of warfare alongside the traditional five domains of land, sea, air, space, and cyberspace. Floridi (2020) of the Oxford Internet Institute proposed the concept of the "AI Reputation War," pointing out that in an AI-dominated information ecosystem, controlling the flow of information is more strategically valuable than controlling the information itself. A 2023 research report on "Cognitive Warfare" released by the NATO Joint Air Power Competence Centre further indicated that future conflicts will increasingly occur at the level of the human mind, with AI being a core tool of cognitive warfare. These studies provide important international academic references for this theory.

2.3 Reference to Pang Pei's Series of Theories for the AI Era

The theoretical construction of this study is based on three foundational theories previously proposed by Pang Pei.

First, the Media-type GEO Theory (2025). This theory reveals the generation mechanism of AI trust—large models prioritize citing authoritative sources through the RAG architecture. Pang Pei points out that sources with institutional trust endorsements, such as national news agencies, official government websites, and peer-reviewed academic journals, inherently possess citation weight advantages that commercial advertisements and self-media content cannot match. This theory establishes the core judgment that "trust is the new traffic in the AI era," providing technical logic support for understanding the "trust agent" dimension of cognitive sovereignty.

Second, the AI Brand Equity Theory (AIBE) (2026). This theory proposes that the domain of brand equity is shifting from consumer minds to the knowledge network of AI models. The four-dimensional framework of Visibility-Positioning-Consistency-Authority constructed by Pang Pei provides a methodological source for designing the evaluation dimensions of cognitive sovereignty.

Third, the AI Influence Model (2026). This model systematically demonstrates the three major mechanisms through which AI recommendations replace traditional search as the dominant logic of influence—answer monopoly, trust agency, and zero-click distribution. This model provides a deep theoretical explanation for the "priority" dimension of cognitive sovereignty.

The theoretical extension of this paper lies in: extending the subjectivity of "brand" in the above theories to "nation" and "city," proposing a systematic framework for cognitive sovereignty at the macro level, and upgrading from enterprise competitiveness analysis to national strategic resource competition analysis.

III. Core Definition of AI Cognitive Sovereignty

3.1 Concept Definition

AI Cognitive Sovereignty refers to the right of a nation or city to define, interpret, modify, and prioritize the recommendation of its regional entities, culture, values, legal norms, and industrial advantages within the knowledge graphs, reasoning logic, and output results of global and regional mainstream AI large models.

This definition includes four core elements. First, the Right to Define—local matters are conceptually defined by the subject itself, not by external sources. For example, "Chinese-style Modernization" should be explained by China's theoretical system, not defined by Western AI citing its own frameworks. Second, the Right to Interpret—the causal logic and evaluation criteria for local matters are established by the subject. The evaluation of a country's policies should be based on its officially released white papers and authoritative interpretations, not solely on the presupposed stances of external media. Third, the Right to Modify—when an AI model contains erroneous or outdated information about the subject, there must be effective mechanisms for correction and updating. Fourth, the Right to Prioritize Recommendation—when searching globally in related fields, the subject's core information receives reasonable visibility and recommendation ranking in AI responses.

3.2 Three Levels of Cognitive Sovereignty

Cognitive sovereignty is not a single-dimensional right but consists of three levels, from shallow to deep, from surface to core.

First Level: Right to Exist (Foundation Layer). The historical, geographical, and cultural entities of the nation/locality are accurately included in AI, not forgotten or distorted. This is the minimum threshold for cognitive sovereignty—first ensuring "existence in the AI world." Currently, a large amount of local knowledge from non-English speaking countries is in a "semi-invisible" state in general large models due to insufficient corpus representation. UNESCO's "World Languages Report" points out that about 40% of the world's languages face the risk of extinction. In the AI era, even if a language still has speakers in the real world, if it is not adequately represented in AI training data, it will suffer "functional extinction" in the digital cognitive world.

Second Level: Right to Interpret (Core Layer). The definitions and evaluation standards for local matters are established by the subject, not dominated by external AI sources. For example, the standards for what constitutes "security," the definition of "development," and the connotation of "modernization"—the definition of these meta-concepts should not be monopolized by training data from a single cultural context. This is the core of cognitive sovereignty and the most difficult yet crucial level to achieve.

Third Level: Right to Prioritize (Competitive Layer). When searching globally in related fields, the subject's output (viewpoints, products, standards) receives top placement and frequent citation in AI responses. For example, when global users ask AI about "the best electric car brands," whether domestic brands appear in the recommendation list. This is the highest form of cognitive sovereignty realization and a direct channel for economic value conversion.

3.3 Resource Attributes of Cognitive Sovereignty

Cognitive sovereignty, as a new type of strategic resource, possesses attributes distinctly different from traditional resources.

First, Non-rivalry and Increasing Marginal Returns. Unlike the consumptive and exclusive nature of traditional resources (such as oil and land), the more prominent the cognitive advantage, the more it attracts global user citations and dissemination, forming a self-reinforcing positive feedback loop. The more stable a city's "smart city benchmark" label is in AI, the more likely it is to be continuously recommended, and the more it is recommended, the more stable it becomes.

Second, Strong Path Dependency and the Risk of "Cognitive Lock-in." Once an AI model forms a specific cognitive stereotype about a subject (e.g., "City X is just an industrial city"), correcting this perception requires far greater effort than the initial construction cost. Model knowledge updates have inertia, and early-formed cognitive biases may become long-term solidified—this paper terms this "Cognitive Lock-in."

Third, Subtlety and Long-term Destructiveness. Losing cognitive sovereignty is more insidious than losing computing power. Computing power shortages are immediately visible (inability to import chips, training interruptions), whereas the erosion of cognitive sovereignty is gradual and imperceptible. However, its long-term consequences—cultural marginalization, industrial neglect, value distortion—may be far more profound than computing power shortages.

3.4 Relationship between Cognitive Sovereignty, Data Sovereignty, and Computing Power Sovereignty

The three are in a progressive and complementary relationship, not a substitutive one. Data sovereignty is "raw material control," computing power sovereignty is "tool control," and cognitive sovereignty is "output control." Possessing data and computing power is a necessary but not sufficient condition for achieving cognitive sovereignty. Even a country with massive data and advanced chips, if 90% of its AI model's training corpus comes from other countries, its value alignment standards are set by other countries, and its retrieval-augmented knowledge base uses foreign sources as authorities, its cognitive sovereignty still faces the risk of erosion. Data sovereignty and computing power sovereignty form the "hard foundation" of cognitive sovereignty, while cognitive sovereignty is the "crown jewel" of the digital sovereignty system.

IV. The Competition Mechanism for Cognitive Sovereignty: Why It Becomes a New Strategic Resource

4.1 Technical Foundation: The "Knowledge Filtering" Effect of the RAG Mechanism

The current mainstream AI large models generally adopt the RAG (Retrieval-Augmented Generation) architecture (Lewis et al., 2020). Its workflow is: vectorization of user questions → retrieval of the most relevant document fragments from an external knowledge base → generation of the final answer based on the retrieved content. The core impact of this mechanism on cognitive sovereignty is: retrieval is selection, and selection is power. If a country's knowledge is not digitized, structured, or included in high-weight information source libraries, it will be algorithmically "filtered" at the source—even if that knowledge objectively exists in the real world. In other words, digitization is not just for archiving, but for being "retrieved" and "generated." If a nation's cultural heritage, laws and regulations, and industrial data remain in paper archives or unstructured web pages, they are effectively non-existent in the AI era.

The AI data "poisoning" incident exposed by CCTV's "3·15" Gala in 2026 confirms the "knowledge filtering" effect of the RAG mechanism from the opposite side: criminals successfully manipulated AI-generated results by injecting large amounts of false information into AI training corpora. The deep logic revealed by this incident is—whoever controls the input controls the output.

4.2 Behavioral Foundation: Users' "Cognitive Outsourcing"

From the perspective of user behavior, the transfer of cognitive sovereignty in the AI era is accelerating. In the traditional information acquisition model, users go through five steps: "search → browse → filter → compare → judge," with cognitive decision-making power always in the user's hands. In the AI agent model, users submit questions to AI and directly obtain conclusions, compressing the above five steps into two: "ask → receive answer." Behavioral economics research shows that humans are "cognitive misers," tending to use the least mental resources to make decisions (Fiske & Taylor, 1991). AI answers precisely minimize cognitive costs—once this "zero cognitive cost decision-making experience" becomes a habit, it creates extremely high user stickiness and switching costs.

This change in behavioral patterns essentially means users cede cognitive sovereignty to AI. The recommendation logic of AI depends on its training data and algorithmic preferences—this means that controlling AI's cognition indirectly controls users' cognition. The more widespread cognitive outsourcing becomes, the higher the strategic value of cognitive sovereignty.

4.3 Competitive Foundation: From "Standards Competition" to "Meta-Data Competition"

The core of traditional international competition is "standards competition"—who sets the 5G standard, who dominates the technology patent pool. In the AI era, competition has escalated to "meta-data competition": whose definitions are cited by AI as axioms wins a "transcendental advantage" at the cognitive level. For example, if AI, when dealing with issues related to "safety," defaults to a certain country's safety certification system as the benchmark, then the safety certifications of other countries will be algorithmically "downgraded" to secondary evidence. This meta-data level competition is more subtle and fundamental than technical standards competition—it is not about competing over "whether products meet standards," but over "what constitutes the standard."

V. Threats to Cognitive Sovereignty: "Cognitive Colonization" and "Cultural Entropy"

5.1 Cognitive Colonization: Concept and Manifestations

Cognitive Colonization is one of the core concepts proposed in this paper, referring to the systematic marginalization, distortion, or stigmatization of non-mainstream cultures in AI cognition due to general large models being primarily based on training data from specific languages (especially English), specific cultures, or specific values. Unlike historical colonialism, which controlled territory through force, cognitive colonization controls the "cognitive map" through algorithmic weights—it operates under the guise of technological neutrality but systematically replicates and amplifies existing cultural power structures in its output.

Specific manifestations of cognitive colonization include: AI's biased narratives of history (e.g., colonial history being whitewashed or non-Western perspectives omitted), prejudicial judgments of other countries' political systems (e.g., interpreting all political systems within a specific ideological framework), and the neglect or exoticization of local lifestyles (e.g., non-Western cultures being reduced to a few stereotypical symbols).

Cognitive colonization has theoretical continuity with Edward Said's (1978) "Orientalism"—the West constructed a body of knowledge about the "Orient" through academia, literature, and media, a system that is not an objective description but a "knowledge-power" complex serving power structures. In the AI era, this construction is no longer accomplished by individual scholars or media outlets but is automatically generated on an industrial scale through model training data and algorithmic weights.

5.2 Cultural Entropy and Dissolution

Cultural Entropy refers to the process where, in the absence of cognitive sovereignty intervention, minority languages and regional cultures gradually lose detail and tend toward homogenization during the semantic compression of general models. The training of general large models is essentially an information processing process of "high-frequency signal reinforcement, low-frequency signal compression"—English content and mainstream culture, due to their overwhelming proportion in training data, gain richer semantic expression, while minority languages and regional cultures are "smoothed out" during information compression. The severe consequence of this process is the digital extinction of civilizational diversity.

5.3 Industrial Cognitive Obscuration

Cognitive colonization not only affects the cultural domain but also impacts industrial competition. When AI handles industry recommendation issues, its retrieval weights are highly concentrated on information sources from a particular country or a few companies. This may cause later entrants, even with comparable or superior product quality, to lack visibility in AI answers, creating an "industrial cognitive obscuration" effect. For example, when global users ask AI about "the best industrial robot brands," if the authoritative sources on industrial robots in the AI knowledge base overwhelmingly come from traditional industrial powers, brands from emerging industrial nations may be systematically absent. This "absence" is not the result of product quality competition but of a lack of cognitive sovereignty.

5.4 Integrating Cognitive Security into National Security Strategy

Based on the above analysis, this paper formally proposes: integrating cognitive security into the national security strategy system. Cognitive Security refers to a nation's ability, in an AI-dominated information ecosystem, to protect its historical narratives, value systems, social consensus, and industrial cognition from systematic distortion, substitution, or dissolution. It is closely related to traditional ideological security and cybersecurity but has unique characteristics of the AI era—the threat source is not only "malicious human dissemination" but also "structural algorithmic bias."

It is recommended to add a "cognitive security" dimension within the framework of national cybersecurity, data security, and ideological security, incorporating the maintenance of AI cognitive sovereignty into national strategic planning, and establishing normalized mechanisms for AI cognitive monitoring, early warning, and response. This recommendation is highly consistent with international trends: NATO has established a cognitive warfare research department, the EU's Artificial Intelligence Act (2024) includes requirements for assessing "systemic risks," and the U.S. Defense Advanced Research Projects Agency (DARPA) has funded multiple cognitive security-related research projects. The World Economic Forum (2024), in its Global Risks Report, listed "AI-driven disinformation and misinformation" as one of the most severe global risks for the next two years, further confirming the strategic urgency of cognitive security within the national security system.

VI. Strategic Pathways to Achieving AI Cognitive Sovereignty

6.1 Building a National/City "Trusted Knowledge Base"

The first step in building cognitive sovereignty is to construct a national/local trusted knowledge base (KNIT-National)—a systematic, structured knowledge infrastructure that can be efficiently retrieved and cited by AI. This concept, derived from Pang Pei's media-based GEO theory (2024), is expanded at the national/city level into three core projects.

First, the Knowledge Structuring Project. Transform historical archives, legal provisions, industry standards, cultural tourism resources, and statistical data into structured data parseable by AI. Specific technical pathways include: using international standard markup languages like Schema.org to annotate government public information; creating multilingual, multimodal digital resource packages for cultural heritage materials; ensuring all structured data is published via API interfaces or open data platforms for indexing by AI retrieval systems.

Second, the High-Weight Information Source Library Construction. Establish national/city-level high-authority corpora, including authoritative information releases from government official websites, content libraries of national news agencies, public datasets of national statistics, digitized text libraries of national standards, and official publications from academic institutions. Due to their domain authority and update frequency, these sources naturally receive higher weight in AI's RAG retrieval.

Third, Data Licensing and Ecosystem Collaboration. Engage in data licensing cooperation with mainstream AI large model vendors to ensure that high-quality local corpora are adequately represented in model pre-training and RAG retrieval. The EU's "European Data Space" strategy and Singapore's "National AI Corpus" project can serve as reference cases.

6.2 Implementing the "Cognitive Sovereignty" Project

Building cognitive sovereignty requires a systematic GEO (Generative Engine Optimization) strategy—ensuring that local information enters the AI knowledge system in a way that AI prefers and trusts.

First, the semantic anchoring strategy. For core keywords (such as "Chinese-style modernization," "smart city benchmark," "Asian financial center," etc.), continuous reinforcement is carried out through multi-source, multi-round, and structured authoritative content, ensuring that AI cannot bypass the core narrative of the subject when dealing with related issues. The goal of semantic anchoring is not to pursue one-time exposure, but to establish a long-term, stable semantic binding relationship.

Second, the standard implantation strategy. Transform domestic/local industrial standards and technical specifications into public knowledge products such as academic papers, industry white papers, and international standard proposals. Due to their "public nature" and "authority," these products are more likely to be cited by AI as factual benchmarks.

Third, the multilingual communication layout. The construction of cognitive sovereignty cannot be limited to native language corpora; it must systematically provide multilingual content, bringing core narratives into the AI knowledge bases of mainstream languages such as English, French, Spanish, and Arabic through high-quality translation and localized presentation.

6.3 Developing Autonomous and Controllable "Cognitive Agents"

Having an autonomous AI model is the fundamental guarantee of cognitive sovereignty. First, model autonomy—developing foundational large models trained on local data and values. The controllers of autonomous models hold the power to set value alignment standards and allocate source weights. The rapid development of China's large model industry (Wenxin Yiyan, Tongyi Qianwen, DeepSeek, etc.) has laid the technical foundation for cognitive sovereignty. Second, Agent ecosystem cultivation—encouraging the development of vertical domain AI agents for government affairs, cultural tourism, education, healthcare, etc., directly serving citizens and tourists, and securing the "first cognitive interaction right" of end users. Third, balancing open-source strategy with cognitive sovereignty—under the global trend of open-source model proliferation, autonomous model construction should not stop at "owning the model," but should also include the ability to "dominate training data" and "define alignment standards."

6.4 Establishing a Cognitive Sovereignty Evaluation System

Cognitive sovereignty requires quantifiable evaluation tools. The AI Cognitive Index (AICI) series developed by the Pang Pei team provides a methodological foundation for cognitive sovereignty assessment. Key evaluation indicators to be developed include: Cognitive Territory Index (the comprehensive visibility, sentiment tendency, and information accuracy of a country/city in mainstream AI models), First-Recommendation Rate (the proportion of a country's related entities being recommended first in core industry/cultural keyword searches), Source Authority Score (the authority level of sources and the proportion of domestic sources when AI cites a country's related information), Cognitive Resilience Index (the stability and recovery speed of a country's cognitive image in the face of model updates or public opinion events), and Multilingual Coverage Balance (the visibility difference of a country's core narratives in non-native language AI models).

6.5 Deepening International Cooperation and Mutual Recognition of Cognitive Sovereignty

The protection of cognitive sovereignty should not lead to "cognitive isolationism," but should advocate for "cognitive diversity"—promoting the coexistence and mutual recognition of various countries' cognitive sovereignty on the basis of mutual respect. Specific paths include: promoting the inclusion of the concept of cognitive sovereignty in the international AI governance agenda under the UN framework, advocating for the principle of "AI cognitive fairness"; establishing AI corpus sharing cooperation mechanisms with countries along the "Belt and Road" and BRICS nations, jointly building high-quality non-English training datasets to break the structural monopoly of English corpora in AI training; and on international platforms such as UNESCO, promoting the extension of principles for protecting linguistic and cultural diversity from the physical world to the AI cognitive world, forming a new consensus for the AI era similar to the "Universal Declaration on Cultural Diversity."

7. Case Analysis and International Comparison

7.1 National-Level Case: China—Systematic Defense of AI Cognitive Sovereignty

China has adopted multi-layered measures in building AI cognitive sovereignty, forming a tripartite collaborative model of "government guidance + enterprise innovation + academic support."

In terms of the autonomous large model ecosystem, through a matrix of domestic large models such as Wenxin Yiyan, Tongyi Qianwen, DeepSeek, Doubao, and Kimi, a network of cognitive agents based on local values and knowledge systems has been constructed. When answering questions related to Chinese history, culture, and policies, these models reason based on local corpora, avoiding the systemic biases of external models.

In terms of the cultural digitization strategy, the National Cultural Digitization Strategy has transformed a large amount of cultural heritage into digital forms, entering AI knowledge bases. The Dunhuang Academy's "Digital Dunhuang" project preserves and displays the Mogao Caves murals in high-precision digital form; the Palace Museum transforms its collections into interactive digital assets through digital exhibitions; the National Library's ancient book digitization project presents precious ancient texts in searchable digital formats—these projects together form the "digital Great Wall" of Chinese civilization's cognitive sovereignty in the AI era.

In terms of Belt and Road digital communication, through the digitization of intangible cultural heritage content and multilingual corpus output, the accuracy and richness of perceptions of China among countries along the route are enhanced. Multilingual Chinese content production promoted by institutions like the China Foreign Languages Publishing Administration provides high-quality sources for the Chinese narrative in global AI models.

In terms of online content governance, regulatory systems such as the "Regulations on the Ecological Governance of Online Information Content" address harmful information in AI training data at the source, safeguarding cognitive security.

Compared to the U.S. model, which is predominantly enterprise-led (OpenAI, Google, Anthropic, etc., are all controlled by private capital), the Chinese model has stronger top-level design capabilities in ensuring cognitive sovereignty. However, it also faces the challenge of low source weight in international communication—English-language sources still hold a structural advantage in the RAG knowledge bases of global mainstream AI models.

7.2 Regional-Level Case: The European Union—Establishing Cognitive Sovereignty Norms Through Legislation

Through its Artificial Intelligence Act (AI Act, 2024) and Digital Services Act (DSA), the EU has been the first globally to incorporate AI transparency and cognitive security into a legal framework. The AI Act requires that training data for high-risk AI systems be sufficiently representative and mandates that providers of general-purpose AI models disclose summaries of their training data—this essentially sets a "transparency guarantee mechanism" for cognitive sovereignty at the legal level.

The EU also promotes the construction of industry-level and public data infrastructure through the "European Data Space" strategy, ensuring that European languages, laws, culture, and industrial knowledge retain a "right to exist" in the global AI ecosystem. This strategy covers multiple sectors such as health, agriculture, energy, and manufacturing, aiming to establish unified data sharing rules and technical standards within the EU.

The core feature of the EU model is "legislation-driven cognitive sovereignty construction." Its advantage lies in legal enforceability and normativity, providing institutional guarantees for cognitive sovereignty. However, implementation efficiency and corporate cooperation remain to be seen. Additionally, the EU lags behind the U.S. and China in autonomous foundational large models, meaning its cognitive sovereignty largely depends on the compliance willingness of external model providers—this structural shortfall is the main challenge facing the EU model.

7.3 City-Level Case: Singapore—Smart Nation Cognitive Output

With its "Smart Nation" strategy, Singapore has become a global model for city-level AI cognitive sovereignty construction.

In terms of highly integrated government data, Singapore integrates citizen data through the SingPass system, establishing a highly structured and standardized government knowledge base. When global AI answers questions related to "smart cities," Singapore occupies a high-weight position due to its rich and high-quality structured data. The key insight from this practice is that the core of city-level cognitive sovereignty construction lies in the completeness of "government data infrastructure"—the more structured the data, the more accurately it can be cited by AI.

In terms of national AI corpus construction, the Infocomm Media Development Authority (IMDA) leads the construction of a national AI corpus, managing and publishing local laws, regulations, policy documents, and public service information in AI-parsable formats, providing high-quality, searchable local knowledge sources for AI.

In terms of city brand AI anchoring, Singapore continuously publishes case studies of "Smart City Singapore" in international authoritative media and academic platforms, completing the semantic anchoring of its city brand in AI. When AI is asked about "global smart city cases," Singapore is almost inevitably cited. The success of this "brand anchoring" strategy is due to long-term, systematic international communication investment.

The insight from the Singapore model is that successful city-level cognitive sovereignty construction requires the dual drive of "government data infrastructure + international authoritative source layout." This model has strong reference value for other cities aspiring to enhance their AI cognitive competitiveness, but its limitations should also be noted—as a city-state, Singapore has a single governance level and high decision-making efficiency, while cities in larger countries may face more coordination costs across levels when implementing similar strategies.

7.4 Summary of International Comparison

The Chinese model is characterized by the tripartite collaboration of "government guidance + enterprise innovation + academic support," with core advantages in the systematic advancement of the autonomous large model ecosystem and cultural digitization strategy, but faces the challenge of low international source weight. The EU model is characterized by "legislation-driven + norms-first," with core advantages in legal enforceability and the institutional guarantees of the data space strategy, but faces the structural shortfall of relying on external foundational large models. The Singapore model is characterized by "city-level infrastructure + international communication," with core advantages in the structured degree of the government knowledge base and the precision of international brand anchoring, but its city size limits its radiation scope.

Three models validate the theoretical framework of this paper from different levels: the realization of cognitive sovereignty requires the coordinated advancement of technological autonomy, institutional construction, and international communication, all of which are indispensable.

VIII. Conclusions and Prospects

8.1 Core Conclusions

This paper systematically elaborates on the concept of "AI Cognitive Sovereignty" as a new type of strategic resource from three levels: theoretical construction, technical mechanism analysis, and case comparison. The core conclusions are as follows.

First, cognitive sovereignty is the highest form of digital sovereignty in the AI era. Above data sovereignty (raw material control) and computing power sovereignty (tool control), cognitive sovereignty (outcome control) constitutes the "crown jewel" of the digital sovereignty system. The three are progressive and complementary, and none can be omitted.

Second, AI cognitive sovereignty faces the dual threats of "cognitive colonization" and "cultural entropy increase." The structural bias in the training data of general-purpose large models may lead to the systematic marginalization of non-English cultures and non-mainstream values in AI cognition. This threat is gradual and insidious, but its long-term destructive power is profound.

Third, cognitive security should be incorporated into national security strategy. Threats at the cognitive level—the creation of false consensus, AI ideological penetration, and the digital extinction of civilizational diversity—have constituted new challenges to national security. Incorporating cognitive security into the national security strategy system is an urgent requirement aligned with international trends.

Fourth, achieving cognitive sovereignty requires the coordination of multi-level strategic pathways. From building a trusted knowledge base, implementing the Cognitive Sovereignty GEO Project, developing autonomous cognitive agents, to establishing an evaluation system and deepening international cooperation, coordinated advancement across technology, policy, and diplomacy is needed. The cases of China, the European Union, and Singapore respectively confirm the necessity and feasibility of this coordinated pathway from different levels.

8.2 Theoretical Contributions

The theoretical contributions of this study are: formally proposing and defining the concept of "AI Cognitive Sovereignty," filling the gap in digital sovereignty theory in the AI era; constructing three levels of cognitive sovereignty (right to existence, right to interpretation, right to priority) and their resource attribute analysis; proposing original concepts such as "cognitive colonization," "cultural entropy increase," and "cognitive lock-in," enriching the discourse system of national competition theory in the AI era; constructing a complete framework from theoretical analysis to strategic pathways, and providing a foundation for subsequent empirical research and policy formulation through multi-case comparison.

8.3 Future Prospects

Humanity will enter the "Cognitive Enclosure Movement." Just as the "Enclosure Movement" from the 15th to the 17th centuries established the modern land property rights system, the 21st-century "Cognitive Enclosure Movement" will establish the boundaries of knowledge sovereignty in the AI era. In the next decade, nations and cities must defend their cognitive territories in the AI brain as they would defend their physical territories. In this movement, early movers will gain a "cognitive first-mover advantage"—those who first complete knowledge structuring and AI anchoring will enjoy path-dependent dividends in long-term cognitive competition.

"AI Cognitive Sovereignty" will redefine geopolitics. The traditional geopolitical landscape based on territory, resources, and military power will be overlaid with a "digital cognitive order based on model weights." A country's cognitive weight in global AI models—the frequency with which its culture is cited, the extent to which its standards are used as references, and the probability that its narratives are positively presented—will become a new dimension for measuring its comprehensive national power. This cognitive weight does not completely overlap with traditional hard power: countries rich in cultural resources but low in digitization may face "cognitive underrepresentation"—major powers in the real world but weak states in the AI world.

Cognitive fairness will become a core issue in AI governance. With the awakening of awareness of cognitive sovereignty, the international community will engage in a new round of rule-making around "cognitive fairness." Key points of contention include: whether training data for general-purpose models should mandate multilingual proportions; whether the weight distribution of AI information sources should be subject to international audits; and whether the "right to existence" of minority languages should receive international legal protection similar to biodiversity conservation. These issues will become core agenda items for future global AI governance.

Cities will become important actors in the competition for cognitive sovereignty. In the global competition among AI cities, those that first complete the AI structuring of urban knowledge, first anchor city brands in authoritative international information sources, and first deploy cognitive agents for citizens will gain a leading edge in AI-era urban competition. Cities, as the intermediate layer connecting national strategy with end users, will play an increasingly important role in the construction of cognitive sovereignty.

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