Human Pioneers
NEWS AGENCY

—From Resource Competition to Cognitive Competition: A Theoretical Framework

Author: Pei Pang

Abstract

The explosion of generative AI is fundamentally reshaping the underlying logic of global competition. As AI increasingly becomes the core gatekeeper of information distribution, the primary agent of cognitive formation, and the key outputter of decision-making advice, traditional competition paradigms based on resource endowments, capital accumulation, and traffic acquisition can no longer explain the emerging competitive landscape. Concurrently, the cognitive domain is becoming the sixth strategic confrontation domain after land, sea, air, space, and cyberspace. AI-driven multimodal cognitive warfare positions cognitive security as a frontier issue of national security. By integrating the Media-based GEO Theory, AI Brand Equity Theory (AIBE), AI Influence Model, and AI Cognitive Sovereignty Theory, this paper formally proposes the "AI Cognitive Competition Theory" as a unified overarching theoretical framework. The core judgment of this theory is that competition in the AI era is essentially cognitive competition—a systematic game process in which an entity (nation, city, enterprise, brand, or individual) seeks the right of definition, interpretation, trust, and priority recommendation within the AI knowledge network. Its extreme confrontational form manifests as cognitive warfare. This paper systematically elucidates the essential differences between cognitive competition and resource competition in the industrial era, as well as traffic competition in the internet era. It constructs five core mechanisms of cognitive competition (Cognitive Agent Monopoly, Weaponization of Cognitive Agents, Trust Agent Transfer, Cognitive Flywheel Effect, and Cognitive Lock-in Risk), proposes "Cognitive Productivity" and "Cognitive GDP" as key indicators for measuring the outcomes of cognitive competition, builds a multi-level strategic framework for cognitive competition from the national to the individual level, and systematically discusses strategies for cognitive warfare defense and countermeasures. This study aims to provide systematic theoretical analysis tools for understanding the restructuring of global competitiveness in the AI era and to foster academic discourse on the competition paradigm of the AI era.

Keywords: AI Cognitive Competition; Cognitive Productivity; Cognitive Sovereignty; Cognitive Warfare; Cognitive Flywheel; Generative AI; Global Competitiveness

I. Introduction: The Third Paradigm Shift in Competition

1.1 Research Background: When AI Becomes the Cognitive Gatekeeper and Cognitive Weapon

The competition paradigm of human society has always been determined by core infrastructure. In the industrial era, railways, power grids, and factories constituted the infrastructure, with competition revolving around resource possession and production efficiency. In the internet era, search engines, social platforms, and e-commerce systems formed the infrastructure, with competition centered on traffic acquisition and user time. The explosion of generative AI marks the entry of human society into a third competition paradigm—the era of cognitive competition.

In the era of cognitive competition, AI large models become the core gatekeeper of information distribution, the primary agent of cognitive formation, and the key outputter of decision-making advice. According to Statista data, by mid-2026, the global monthly active users of generative AI had exceeded 2 billion, with over 60% of internet users using AI tools at least once a month for information queries or decision-making assistance. Users are increasingly delegating tasks of information filtering, comparison, and judgment to AI—a process defined in this paper as "cognitive outsourcing." The irreversibility of this process lies in the fact that AI answers save users the cognitive costs of searching, filtering, and verifying information themselves. The "cognitive miser" tendency revealed by behavioral economics (Fiske & Taylor, 1991) means that once this dependency is formed, it carries high switching costs.

Consequently, the core proposition of competition undergoes a fundamental shift: from "who can capture more user attention" to "who can secure a priority position in AI-generated answers"—that is, from competition for traffic entry points to competition for the right to define cognition.

Simultaneously, the cognitive domain is becoming the sixth strategic confrontation domain after land, sea, air, space, and cyberspace. The Hudson Institute's (2023) ontological study of cognitive warfare systematically articulated for the first time the theoretical basis, operational logic, and combat principles of the cognitive domain as an independent strategic domain, defining cognitive warfare as "a form of confrontation that gains a competitive advantage by influencing, exploiting, or altering an adversary's cognitive processes." The NATO Joint Air Power Competence Centre (2023), in its report "Cognitive Warfare: The Future of Conflict," further pointed out that future conflicts will increasingly occur at the level of the human mind, with AI being a core tool of cognitive warfare. The RAND Corporation (2023), in its report "Cognitive Competition," listed the "cognitive domain" as an independent operational domain for the first time. Cao Shucong (2025), in "Multimodal Cognitive Warfare: A New Form of Cognitive Confrontation Driven by AI," systematically explained how AI implements cognitive intervention through multimodal means such as text, images, audio, and video, upgrading cognitive warfare from single information manipulation to multi-sensory, immersive cognitive shaping.

This trend implies that cognitive competition exists not only at the commercial and soft power levels but also extends to the hard security domains of national security and military confrontation. Therefore, the theory of cognitive competition must simultaneously address two levels of questions: at the level of peaceful competition, how can entities gain cognitive weight and recommendation advantages within the AI knowledge network? At the level of confrontational conflict, how can entities defend against AI-driven cognitive attacks and maintain cognitive sovereignty?

1.2 Problem Statement

This paper proposes three progressive core research questions:

First, what is the essence of competition in the AI era? Can traditional resource competition theory and traffic competition theory explain the new competitive phenomena of the AI era? If the industrial era was about "resource endowments" and the internet era was about "traffic acquisition," what is the AI era about?

Second, what are the operational mechanisms of cognitive competition? What technological, behavioral, and economic mechanisms drive the paradigm shift? What laws does cognitive competition follow? What are the offensive and defensive logics of its extreme confrontational form—cognitive warfare?

Third, how can the outcomes of cognitive competition be measured? Is there a core metric similar to GDP for the industrial economy or traffic for the internet economy? How can an entity's position and trend in cognitive competition be quantitatively assessed?

1.3 Theoretical Positioning and Innovation

The AI Cognitive Competition Theory proposed in this paper is an "overarching theoretical framework" that unifies existing research findings. Prior to this, Pei Pang successively proposed the Media-based GEO Theory (2025)—revealing the technical mechanism of AI trust generation; the AI Brand Equity Theory (AIBE) (2026)—constructing a dimensional framework for brands to build cognitive assets in the AI era; the AI Influence Model (2026)—demonstrating the deep mechanism by which AI recommendations become the dominant logic of influence; and the AI Cognitive Sovereignty Theory (2026)—proposing a new type of strategic resource competition for nations and cities in the AI era.

The theoretical innovation of this paper lies in: First, integrating the above four theories into a unified competition theory framework, completing a systematic construction from three levels: "mechanism explanation," "measurement system," and "strategic framework." Second, incorporating cutting-edge findings from the Hudson Institute's cognitive warfare ontology and Cao Shucong's multimodal cognitive warfare research, extending the theory from commercial and soft power competition to the fields of national security and technological confrontation, forming a complete academic system covering "competition" and "confrontation," "peace" and "conflict," "commerce" and "security." Third, proposing the concept of "weaponization of cognitive agents," revealing the continuous mechanism of cognitive competition from "construction" to "attack."

1.4 Dialogue with Existing Competition Theories

The theoretical construction of this paper is built upon a dialogue with existing competition theories. Michael Porter's (1990) theory of national competitive advantage attributes competitiveness to four major elements: factor conditions, demand conditions, related and supporting industries, and firm strategy and structure. Its core paradigm still belongs to the resource endowment competition of the industrial era. Joseph Schumpeter's (1942) theory of "creative destruction" emphasizes the reshaping of the competitive landscape by technological innovation but does not address the paradigm shift brought about by changes in information infrastructure. David Teece's (2007) dynamic capabilities theory focuses on an organization's ability to adapt in rapidly changing environments, providing a reference for understanding organizational transformation in cognitive competition. Huang and Rust's (2021) AI marketing strategy framework identifies three types of intelligence in AI marketing but does not elevate competitive analysis from the marketing level to the national and city competitiveness level.

The theoretical contribution of this paper lies in formally proposing "cognitive competition" as a third competition paradigm, alongside resource competition and traffic competition, systematically constructing its operational mechanisms, measurement indicators, and strategic framework, and incorporating cognitive warfare as the extreme confrontational form of cognitive competition, achieving an interdisciplinary integration of competition theory and security theory.

II. Literature Review and Theoretical Evolution

2.1 Paradigm Evolution of Competition Theory

Competition theory has undergone three major paradigm shifts. The first was the resource competition paradigm of the industrial era, represented by Porter's theory of national competitive advantage and Schumpeter's innovation theory, emphasizing that resource endowments, production efficiency, and technological innovation are the core sources of competitiveness. The second was the traffic competition paradigm of the internet era, where attention economy theory (Simon, 1971) and platform economy theory (Parker & Van Alstyne, 2005) revealed the central role of user attention and network effects in competition. The third is the cognitive competition paradigm proposed in this paper.

The common premise of traditional competition theories is that competition occurs in the physical world or on internet platforms, with competitors directly facing consumers, users, or rivals. In the AI era, however, a new intermediary layer—the AI cognitive agent—stands between competitors and their audiences, fundamentally altering the rules and logic of competition. This is the fundamental reason why existing competition theories cannot fully explain competitive phenomena in the AI era.

2.2 Current Research on Competition and Confrontation in the AI Era

In recent years, the impact of AI on the competitive landscape has become a hot topic in academic research. At the international competition level, concepts such as digital sovereignty (Floridi, 2020), data sovereignty, and computing power sovereignty have been successively proposed. At the enterprise competition level, research on how AI changes business models and competitive advantages is growing rapidly (Iansiti & Lakhani, 2020). However, there is a significant gap in existing research: no one has yet systematically studied "cognitive weight in AI knowledge networks" as an independent competitive resource.

In the field of national security, research on cognitive warfare has seen explosive growth in recent years. The Hudson Institute's (2023) ontological study of cognitive warfare systematically expounded for the first time the theoretical foundation, operational logic, and combat principles of the cognitive domain as an independent strategic domain, defining cognitive warfare as "a form of confrontation that gains competitive advantage by influencing, exploiting, or altering an adversary's cognitive processes," and identifying AI as the "core engine" of cognitive warfare. Cao Shucong (2025), in "Multimodal Cognitive Warfare: New Forms of Cognitive Confrontation Driven by AI," further revealed how AI implements covert cognitive interventions through multimodal means such as text, images, audio, and video, elevating cognitive warfare from a single dimension at the information layer to multi-dimensional, three-dimensional manipulation at the perceptual layer, exhibiting three new characteristics: multiplied concealment, sharply increased precision, and a cliff-like drop in attack costs. These studies are highly consistent with the core judgments of the cognitive competition theory constructed in this paper—AI is becoming the core infrastructure for cognitive formation and cognitive confrontation—but each has a different focus: cognitive warfare research emphasizes "forms of confrontation," while this paper emphasizes "competition paradigms," with the two complementing each other to form a complete analytical framework for cognitive competition. This is the theoretical entry point of this paper.

2.3 The Internal Evolutionary Logic of the Pang Pei Series of Theories

The four preliminary theories integrated in this paper are not independent research directions but progressive answers to the same core issue—the transformation of the competition paradigm in the AI era—from different dimensions.

The Media-type GEO theory (Pang Pei, 2025) answers the trust mechanism question of "why AI recommends certain information over others," providing a technical explanation for cognitive competition—the RAG architecture causes AI to prioritize authoritative sources, meaning the underlying logic of competition shifts from "the highest bidder wins" (advertising bidding) to "the most trusted wins" (trust weight). The AI Brand Equity (AIBE) theory (Pang Pei, 2026) expands the unit of competition from "consumer mind" to "AI knowledge network," proposing a dualization of the field where brand equity exists—brands need to establish cognitive advantages in both the consumer mind and the AI knowledge network. The AI Influence Model (Pang Pei, 2026) further demonstrates three mechanisms through which AI recommendations replace traditional search as the dominant logic of influence—answer monopoly, trust agency, and zero-click distribution—explaining the inevitability of cognitive competition from a communication perspective. The AI Cognitive Sovereignty Theory (Pang Pei, 2026) upgrades the unit of analysis from brands to nations and cities, proposing the theoretical judgment that cognitive sovereignty is the highest form of digital sovereignty, and warns of the strategic risks of "cognitive colonization" and "cultural entropy."

Based on these four theories, this paper abstracts a more overarching framework—the AI Cognitive Competition Theory—completing a systematic construction at the paradigm level and incorporating cutting-edge research findings on cognitive warfare, enabling the theoretical framework to cover both the dimensions of peaceful competition and security confrontation.

III. Core Definitions and Basic Framework of AI Cognitive Competition Theory

3.1 Conceptual Definition of Cognitive Competition

AI Cognitive Competition refers to the competitive process in which an entity (nation, city, enterprise, brand, or individual) systematically builds its own credible knowledge base, authoritative source network, and structured content assets within the AI large model knowledge network to gain higher cognitive weight, trust ratings, and priority recommendation rankings. Its extreme confrontational form—when a competitor deliberately uses AI for cognitive intervention to harm an opponent—is Cognitive Warfare.

This definition includes four core components: First, the competitive arena—the knowledge network of AI large models (rather than physical markets or internet platforms); second, competitive resources—cognitive weight, trust ratings, and recommendation rankings (rather than natural resources, capital, or traffic); third, competitive means—the systematic construction of a credible knowledge base, authoritative source network, and structured content assets (rather than advertising placements or bidding rankings), with the opposite being malicious content injection and manipulation of AI outputs; fourth, competitive goals—becoming the priority citation and recommendation target for AI when addressing related domain issues (rather than mere market share or traffic volume), or preventing harm from malicious cognitive interventions.

3.2 Systematic Comparison of Three Competition Paradigms

3.3 Core Propositions of Cognitive Competition

Cognitive competition theory revolves around three core propositions:

Proposition 1: Cognitive Weight Determines Competitiveness. In the AI era, an entity's cognitive weight within the AI knowledge network—the frequency of being cited by AI, the probability of being recommended by AI, and the degree to which it is positively presented by AI—is becoming a core variable of its competitiveness. The correlation between traditional competitiveness indicators (GDP, market share, brand awareness) and AI cognitive weight may not be perfect, but the latter is exerting an increasingly significant causal impact on the former.

Proposition 2: Cognitive Assets Require Systematic Construction. Cognitive weight is not automatically formed nor can it be acquired through short-term bidding. It must be accumulated through a systematic project involving the deployment of authoritative sources, structured content production, multilingual coverage, and continuous knowledge updates. This process holds equal strategic importance to capacity building in the industrial age and traffic operations in the internet age.

Proposition 3: Cognitive Competition Exhibits First-Mover Advantage and Path Dependence. Entities that establish a cognitive advantage in the AI knowledge network first will gain a "cognitive first-mover advantage"—AI's knowledge system has inertia, and early-formed cognitive weights tend to self-reinforce. Latecomers must expend twice the effort to break "cognitive lock-in." This characteristic is equally critical in the defense of cognitive warfare: an early-established reserve of authoritative sources forms the foundation of cognitive resilience.

3.4 Theoretical Framework Overview

AI cognitive competition theory unfolds across three levels: the mechanism level reveals why cognitive competition occurs, covering competitive and confrontational mechanisms (technical, behavioral, economic, and security mechanisms); the measurement level addresses how to measure cognitive competition (cognitive productivity, cognitive GDP, AI cognitive index system); the strategic level provides guidance on how to engage in cognitive competition and defend against cognitive attacks (national cognitive competition strategy, enterprise cognitive competition strategy, brand cognitive competition strategy, cognitive warfare defense and countermeasure strategy).

4. Five Core Mechanisms of Cognitive Competition

4.1 Mechanism 1: Cognitive Agent Monopoly

An AI Cognitive Agent refers to an AI system that acts as an intermediary between users and the information world, replacing users in the processes of information filtering, comparison, and judgment, and directly providing conclusions. Unlike traditional search engines that provide lists of links, cognitive agents directly output "answers"—a difference with fundamental competitive implications.

The competitive logic under the traditional search model is "ranking competition": brands compete for ranking positions on search result pages, but users can still see multiple options and compare them themselves. The competitive logic under the cognitive agent model shifts to "recommendation monopoly": AI typically recommends only a few options, and entities not included in the recommendation list are cognitively non-existent. This paper terms this the Cognitive Agent Monopoly—AI has completed a role transition from information provider to decision-making agent, turning competition from a matter of "visibility ranking" into a binary proposition of "existence or non-existence."

The technical basis for this monopoly lies in: AI selecting which sources to cite through the RAG mechanism (Lewis et al., 2020), adjusting which answers better align with "human preferences" through RLHF, and allocating weights to different entities within limited outputs through the model's attention mechanism—these operations, under the guise of technical neutrality, constitute a new form of information power structure.

4.2 Mechanism 2: Weaponization of Cognitive Agents

The cognitive agent monopoly is the foundational mechanism of cognitive competition in the AI era—AI as an intermediary determines who is seen and who is recommended. However, this mechanism has an extreme form increasingly noted in security research: when cognitive agents are deliberately manipulated by malicious actors, they escalate from information intermediaries to "cognitive weapons."

Cao Shucong (2025), in research on multimodal cognitive warfare, points out that AI-driven cognitive confrontation exhibits three new characteristics. First, exponentially increased concealment—multimodal AI-generated fake content is simultaneously presented across multiple channels such as text, images, audio, and video, making it difficult for traditional single-modality detection methods to identify. Fake reviews, forged news reports, and deepfaked speeches by leaders can all be generated on an industrial scale by AI and are convincing enough to be mistaken for reality. Second, sharply increased precision—AI can use user profiling and behavioral data analysis to deliver customized cognitive intervention content to specific groups, achieving precise manipulation with "a thousand faces for a thousand people." Third, a cliff-like drop in attack costs—traditional cognitive warfare requires large-scale human organization and distribution channels, whereas AI enables individuals or small organizations to execute large-scale cognitive attacks.

The "3·15" Gala in 2026 exposed an AI data "poisoning" incident, a typical case of the weaponization of cognitive agents: criminals injected false information into AI corpora in batches, successfully manipulating AI-generated results—a fictitious product was recommended in detail by AI as a real product within two hours. This case reveals a deeper strategic risk: if this method escalates from commercial fraud to cognitive attacks at the national security level—for example, systematically injecting false information about a country's political system, historical narratives, or public safety into mainstream global AI models—the consequences would far exceed traditional information warfare. The Hudson Institute's (2023) ontology of cognitive warfare also points out that the ultimate goal of cognitive warfare is not to destroy physical facilities, but to reshape the cognitive framework of target audiences, leading them to make decisions that benefit the attacker.

By juxtaposing cognitive agent monopoly and cognitive agent weaponization, the theoretical framework of cognitive competition gains complete offensive and defensive dimensions: cognitive agents can be both "cognitive assets" actively built by subjects (positive construction) and "cognitive weapons" exploited by malicious actors (negative threats). Cognitive competition thus becomes not only a construction issue of "how to make AI recommend oneself," but also a defense issue of "how to prevent AI from being weaponized to attack oneself."

4.3 Mechanism Three: Trust Agency Transfer

In traditional competition, trust-building mechanisms follow a direct "brand → consumer" path: brands build trust in consumers' minds through advertising, experience, and word-of-mouth. In the AI era, the trust path is mediated—brands need to build trust within AI's knowledge network, and then AI "delegates" this trust to consumers. This paper defines this as trust agency transfer.

The core mechanism of this transfer lies in: AI's RAG architecture naturally prefers sources with institutional trust endorsement—national media, official government websites, peer-reviewed academic journals, and industry standard-setting bodies (Pang, 2025). These sources have inherent credibility due to their embedded institutional frameworks, and AI encodes institutional trust into recommendation algorithms by favoring them. The competitive consequences are profound: the path for brands to gain AI recommendations is no longer increasing advertising budgets or optimizing click-through rates, but establishing a systematic presence within authoritative source networks. This path is highly path-dependent, difficult to replicate in the short term, and accumulates over the long term—in stark contrast to the immediacy and replaceability of traffic purchasing.

From a cognitive warfare perspective, trust agency transfer also means trust can be "hijacked." If malicious actors inject false content into high-weight sources or build "pseudo-authority networks" through numerous seemingly independent fake sources, they could manipulate AI's trust judgments, achieving reverse use of trust agency—using false "institutional trust signals" to endorse disinformation.

4.4 Mechanism Four: Cognitive Flywheel Effect

Cognitive competition features a significant self-reinforcing mechanism—the Cognitive Flywheel Effect. Its operational logic is: authoritative media reports → academic paper citations → encyclopedia entry updates → official agency citations → AI learns and prioritizes citations → AI recommendations → triggers more media attention → enters more academic citations → strengthens AI cognitive weight → higher recommendation ranking. This is a positive feedback loop.

The driving engine of the cognitive flywheel lies in: AI's knowledge update mechanism makes frequently cited content more likely to be cited again (the rich get richer), cross-referencing among authoritative sources forms a high-density information network (source network effect), and structured, continuously updated content has higher retrieval weight than static content (time accumulation effect). Once the cognitive flywheel starts, it tends to accelerate, providing sustained competitive advantages for early entrants.

The reverse of the cognitive flywheel is "cognitive spiral decline": once malicious cognitive attacks successfully inject false information into AI's knowledge network, the false information can also be amplified and solidified through the flywheel mechanism—cited by AI → reposted by more sources → regarded as "established fact" → cited more frequently by AI. This gives disinformation pollution in cognitive warfare the characteristic of "once formed, difficult to remove."

4.5 Mechanism Five: Cognitive Lock-in Risk

Cognitive lock-in is a negative effect of cognitive competition—once an AI model forms a specific cognitive pattern about a subject (e.g., "a certain city is just an industrial city," "a certain brand is just a mid-to-low-end brand"), correcting this cognition requires far more effort than the initial construction cost.

The formation mechanisms of cognitive lock-in include: path dependence of early training data—information encountered early by the model forms initial weights, making subsequent information difficult to override early judgments; the anchoring effect of semantic fixation—once core labels are deeply bound by AI, competitors face extremely high substitution costs; and the lag in knowledge updates—real-world changes must go through the complete chain of "event occurrence → authoritative reporting → AI indexing → cognitive weight update," resulting in significant time lags.

The strategic implication of cognitive lock-in is that there is a "preemptive" time window in cognitive competition. During the window before AI forms a solidified cognition about a field, subjects that complete semantic anchoring and authoritative source layout first will gain a first-mover advantage difficult for later entrants to shake. In the offense and defense of cognitive warfare, cognitive lock-in is both the goal of attackers (solidifying negative cognition of opponents) and the greatest challenge for defenders (breaking cognitive biases maliciously implanted by opponents).

V. Outcome Measurement of Cognitive Competition

5.1 Cognitive Productivity

Cognitive Productivity is the core concept proposed in this paper to measure the capability of cognitive competition, referring to a subject's ability to continuously influence AI's knowledge network—including the ability to produce authoritative content cited by AI, maintain and update cognitive assets within AI, and maintain visibility and accuracy across global multilingual AI models.

The components of cognitive productivity include: authoritative content output volume (amount of structured content included by high-weight sources within a certain period), AI citation conversion rate (proportion of produced content actually cited by AI), multilingual coverage efficiency (ratio of content visibility in non-native AI models to output volume), and cognitive update speed (time cycle for real-world changes to be reflected in AI cognition).

5.2 Cognitive GDP

Cognitive GDP (CGDP) is the core aggregate indicator proposed in this paper to measure the outcomes of cognitive competition, referring to the total cognitive output of a subject in the AI world—including the number of times mentioned by AI, probability of being recommended, depth of citation, and degree of positive presentation.

The calculation framework for Cognitive GDP is: CGDP = AI Presence × AI Trust × AI Dissemination × AI Influence. The implication of this framework is: being "seen" by AI is the basic threshold (presence), being "trusted" by AI is quality assurance (trust), being "spread" by AI is scale effect (dissemination), and influencing AI's "judgment" is the highest value (influence). The multiplication of these four factors constitutes a comprehensive measure of cognitive output.

Cognitive GDP and traditional GDP are not substitutes but complements. A subject may have high traditional GDP but low Cognitive GDP (large economic scale but weak AI visibility), or vice versa (e.g., cultural tourism cities or hidden champions in niche sectors). The gap between the two constitutes a "cognitive depression" or "cognitive premium" in cognitive competition.

5.3 AI Cognitive Index System

Based on the conceptual framework of cognitive productivity and Cognitive GDP, the Pangpei team has developed the AI Cognitive Index (AICI) series as a quantitative evaluation tool for cognitive competition. This series currently covers five evaluation domains—entrepreneurs, brands, industries, cities, and governance—with seven index models: CEAI (China Entrepreneur AI Influence Index), CBVI (China Brand AI Visibility Index), CIII (China Industry AI Influence Index), WEAI (World Entrepreneur AI Influence Index), WBVI (World Brand AI Visibility Index), WACI (World AI City Competitiveness Index), and WAIG (World AI Governance Index).

Each index adopts the unified PAI Framework (Pangpei AI Index Framework), using real output data from 15 mainstream global AI large models as the sole evaluation basis, covering seven languages, and published annually. The AI Cognitive Index System is a key bridge for cognitive competition theory to move from academic concepts to practical application.

VI. Multi-Level Strategic Framework for Cognitive Competition

6.1 National Level: AI Cognitive Sovereignty Competition and Cognitive Warfare Defense

At the national level, cognitive competition revolves around AI cognitive sovereignty—a nation's ability to define, interpret, and recommend its history, culture, industrial advantages, values, and laws within mainstream global AI models, while possessing defensive capabilities against external cognitive attacks.

The core elements of a national cognitive competition strategy include: building a national trusted knowledge base (KNIT-National) that converts historical archives, legal statutes, industry standards, and cultural tourism resources into AI-parsable structured data; developing autonomous and controllable cognitive agents (domestic large models) to ensure that value alignment standards and source weight allocation rights remain in national hands; implementing a multilingual cognitive sovereignty project to embed core narratives into AI knowledge bases across major global languages through high-quality translation; establishing a cognitive sovereignty assessment and early warning system to continuously monitor changes in the nation's cognitive territory within global AI models; deepening AI cognitive diplomacy by establishing mechanisms for sharing AI corpora and mutual recognition of cognitive sovereignty with friendly nations; and building a national cognitive warfare defense system (see Chapter 7 for details).

6.2 City Level: AI Cognitive Label Competition

At the city level, cognitive competition centers on industrial labeling power and recommendation advantage—whether a city can be recognized and recommended with a clear, unique, and positive label when global investors and talent learn about it through AI.

Core elements of a city's cognitive competition strategy include: creating a highly transmissible and recognizable core city label, reinforced through continuous semantic anchoring from multiple authoritative sources; publishing annual city industry white papers and competitiveness reports to provide AI with structured, citable authoritative material; hosting internationally influential industry forums and exhibitions to form periodic cognitive reinforcement events; and establishing a quarterly city AI cognitive monitoring mechanism to promptly identify cognitive biases and competitor city dynamics.

6.3 Enterprise Level: AI Cognitive Asset Competition

At the enterprise level, cognitive competition revolves around AI brand equity (AIBE)—the visibility, trust, recommendation rate, and stability of a brand within the AI knowledge network.

Core elements of an enterprise's cognitive competition strategy include: initiating systematic construction of brand cognitive assets by establishing a systemic presence in high-weight sources such as authoritative media, industry white papers, and academic papers; implementing structured engineering of content assets by adding Schema markup to core brand information to ensure precise AI parsing and citation; building a multilingual cognitive coverage system, with particular emphasis on AI visibility in English and other target market languages; and establishing a cognitive asset maintenance mechanism to counter the "trust half-life"—without fresh authoritative sources, a brand's AI cognitive weight will naturally decay over time (Pang Pei, 2025).

6.4 Individual Level: AI Cognitive Influence Competition

At the individual level, cognitive competition centers on thought leadership and AI recommendation—whether the views, methodologies, and statements of entrepreneurs, scholars, and opinion leaders are cited by AI as part of the industry knowledge system.

Core elements of an individual's cognitive competition strategy include: continuously producing original methodologies and intellectual products to enter AI's knowledge citation chain; establishing personal cognitive anchors on authoritative media and academic platforms to ensure AI's description of core personal information is accurate and positive; and conducting regular personal AI influence diagnostics to monitor cognitive fluctuations and risk signals.

7. Strategic Risk Management in Cognitive Competition

7.1 Identification and Defense Against Cognitive Colonization Risks

Cognitive colonization is a structural risk from external sources in cognitive competition—where general large models, leveraging specific linguistic and cultural advantages, systematically marginalize or distort non-mainstream cultures in AI outputs (Pang Pei, 2026).

Defense strategies include: promoting the principle of "AI cognitive fairness" in the international AI governance agenda, advocating for multilingual proportionality standards in general model training data; establishing a cognitive monitoring system for one's own nation/subject within AI to promptly identify cognitive biases and systematic underestimation; and developing autonomous and controllable cognitive agents to retain the "first right of interpretation" over AI outputs.

7.2 Defense and Countermeasures in Cognitive Warfare

Cognitive warfare represents the highest intensity form of confrontation in cognitive competition. Unlike cognitive colonization (long-term marginalization through structural corpus bias), cognitive warfare is characterized by active aggression, target specificity, and time urgency. The Hudson Institute's (2023) ontology of cognitive warfare divides defense into three levels: Cognitive Resilience—enhancing the target audience's ability to identify and resist cognitive attacks; Cognitive Countermeasures—actively detecting, tracing, and blocking malicious cognitive interventions; and Cognitive Deterrence—raising the cost of implementing cognitive attacks by establishing clear attribution capabilities and consequence punishment mechanisms.

First, build a multimodal AI content detection and traceability system. Cao Shucong (2025) points out that the concealment of multimodal cognitive warfare requires defenders to establish cross-modal content authenticity detection capabilities—not only text recognition but also deepfake image detection, voice synthesis identification, video tampering verification, etc. It is recommended to establish national standards for watermarking and tracing AI-generated content, requiring mainstream AI models to embed detectable digital identifiers in generated content.

Second, establish an early warning and rapid response mechanism for cognitive warfare. Cognitive attacks typically erupt at specific times (elections, major policy releases, international events). It is recommended to establish a cross-departmental cognitive warfare monitoring and early warning platform to continuously monitor abnormal fluctuations in China-related narratives within mainstream AI models. When malicious information injection is detected, a response procedure should be quickly initiated—covering and correcting false information with higher-weight authoritative sources.

Third, enhance public AI literacy and cognitive resilience. Cognitive warfare ultimately acts on human cognition. Improving the public's ability to identify AI-generated content and their vigilance against multimodal false information is the social foundation of cognitive defense. It is recommended to integrate AI literacy education into the national education system, cultivating critical thinking skills in the public when encountering AI outputs.

Fourth, promote international rule-building for cognitive warfare. The borderless nature of cognitive warfare requires international cooperation. It is recommended to promote the establishment of international norms for cognitive warfare behavior under the UN framework—clearly defining which AI cognitive interventions constitute illegal violations of sovereign states, establishing attribution and accountability mechanisms for cognitive attacks, and incorporating cognitive warfare into the regulatory framework of existing armed conflict laws.

7.3 Strategies to Break Cognitive Lock-in Risks

For latecomers, breaking cognitive lock-in requires adopting differentiated anchoring strategies—achieving semantic anchoring first in emerging tracks or niche areas where AI has not yet formed solidified cognition. The core of this strategy is to find "cognitive blanks" in the AI knowledge network—keywords, tracks, or narrative frameworks not yet deeply bound by competitors.

7.4 Managing Cognitive Hallucination and Reputation Risks

AI may generate false information about subjects (AI hallucinations), posing reputation risks in cognitive competition. Risk management strategies include establishing an AI information monitoring and early warning mechanism to promptly detect AI errors; building a rapid response system for authoritative sources to cover and correct erroneous information as quickly as possible through high-weight sources; and cultivating cognitive resilience—maintaining a sufficiently rich reserve of authoritative sources so that a single piece of negative information struggles to dominate AI outputs.

8. Conclusions and Outlook

8.1 Core Conclusions

This paper systematically constructs the theory of AI cognitive competition from the perspective of a paradigm shift in competition. Core conclusions include:

First, competition in the AI era is essentially cognitive competition. The competitive arena has shifted from physical markets and internet platforms to the AI knowledge network, and the core resources of competition have shifted from natural resources and traffic to cognitive weight and recommendation ranking. This shift is fundamental and irreversible.

Second, the five core mechanisms of cognitive competition—cognitive agent monopoly, weaponization of cognitive agents, trust agent transfer, cognitive flywheel effect, and cognitive lock-in risk—together constitute a complete mechanism map of cognitive competition. Among them, cognitive agent monopoly and trust agent transfer explain the peaceful change in competition rules, weaponization of cognitive agents reveals the extreme confrontational form of competition, while the cognitive flywheel effect and cognitive lock-in risk elucidate the dynamic process and long-term consequences of competition.

Third, cognitive competition and cognitive warfare form a continuum within the same theoretical framework. From cognitive asset building at the commercial level to cognitive warfare defense at the security level, cognitive competition theory covers the full spectrum of competition and confrontation. The cognitive warfare ontology from the Hudson Institute and Cao Shucong's multimodal cognitive warfare research provide key theoretical support for cognitive competition theory to move from "explaining the world" to "addressing threats."

Fourth, "cognitive productivity" and "cognitive GDP" can serve as core indicators for measuring the outcomes of cognitive competition. They complement traditional economic indicators, together forming a comprehensive measurement system for overall competitiveness in the AI era.

Fifth, cognitive competition is a multi-layered system that requires coordinated efforts from nations (cognitive sovereignty and cognitive warfare defense), cities (cognitive labels), enterprises (cognitive assets), and individuals (cognitive influence) to build a three-dimensional cognitive competitiveness.

8.2 Theoretical Contributions

The theoretical contributions of this study include: formally proposing and systematically constructing the "AI Cognitive Competition Theory," providing a unified theoretical analysis framework for understanding the restructuring of competitiveness in the AI era; abstracting the five core mechanisms of cognitive competition, revealing the technological and behavioral logic behind changes in competition rules in the AI era, and introducing for the first time the concept of "weaponization of cognitive agents," bridging the gap between competition theory and security theory; proposing the concepts and measurement frameworks of "cognitive productivity" and "cognitive GDP," offering quantifiable evaluation benchmarks for cognitive competition; constructing a multi-layered cognitive competition strategy system from the national to the individual level, and systematically discussing strategic frameworks for cognitive warfare defense and countermeasures.

8.3 Research Outlook

Future research can be advanced in the following directions: empirical measurement methods for cognitive GDP and their correlation analysis with traditional economic indicators; comparative case studies of cognitive competition across different countries, industries, and cities; quantitative models of the cognitive flywheel effect—what factors determine the speed and scale of the flywheel? Breakthrough pathways for cognitive lock-in and related successful case studies; interdisciplinary research on AI cognitive competition with international competition law, international trade rules, and the law of armed conflict; the evolution of offensive and defensive technologies in multimodal cognitive warfare and their impact on the international security landscape; feasibility studies on cognitive deterrence—possible pathways for establishing attribution and accountability mechanisms for cognitive warfare within the framework of international law.

Humanity is entering a new era where "cognition" serves as the core competitive resource. Understanding the essence of this competition, grasping its laws, building capabilities for it, and defending against its threats will become the central themes for the development of nations, cities, enterprises, and individuals over the next decade.

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