Human Pioneers
NEWS AGENCY

Trust Verification, Cognitive Parasitism, and Dynamic Decay: Theoretical Extension and Model Deepening of AI-Based Brand Equity (AIBE)

Pei Pang

Abstract

As generative artificial intelligence becomes the core intermediary for users to access information, the domain of brand equity is shifting from consumer minds to the knowledge networks of AI large models. The AI-Based Brand Equity (AIBE) theory proposed by Pang (2025) initially defines the cognitive dimensions and maturity ladder of brands within AI systems. However, theoretical gaps remain in the closed-loop mechanism of trust transmission, the laws of dynamic asset decay, the formation pathways of high-level assets, and the concretization of the knowledge foundation. This paper systematically deepens the theory at these critical nodes: It introduces the H-C-A (Human-Brand-AI Agent) trust transmission model to complete the verification loop from "AI adoption" to "consumer adoption," adding "verifiability" as a fifth dimension of AIBE. Drawing on the concept of half-life in physics and signal attenuation theory, it proposes the "trust half-life" and an AIBE asset depreciation model, revealing the dynamic decay patterns of brand AI assets during model iterations. Using the mechanisms of "cognitive parasitism" and "standard parasitism," it deeply explains the transition path from L5 pyramid top from "cited entity" to "standard setter." It concretizes the KNIT foundation into an engineering framework of "brand knowledge API-ization," comprising core components such as a structured fact database, verifiable certification chain, and multimodal metadata. These theoretical extensions upgrade AIBE from a static cognitive framework to a new brand equity paradigm with dynamic explanatory power, closed-loop completeness, and engineering operability, providing a more practically guiding theoretical tool for brand management in the era of generative AI.

Keywords: AI-Based Brand Equity; AIBE; Trust Verification; Cognitive Parasitism; Trust Half-Life; H-C-A Model; KNIT; Brand Knowledge API-ization

I. Introduction

1.1 Research Background and Problem Statement

The launch of ChatGPT at the end of 2022 marked a fundamental reshaping of information access by generative artificial intelligence. By the end of 2025, global monthly active users of generative AI have exceeded 1.8 billion, with over 60% of internet users using AI tools at least once a month for information queries, decision support, or content consumption [1]. In this wave, AI large models are quietly replacing traditional search engines, becoming the "first cognitive gatekeeper" between brands and consumers. When users ask "Which new energy vehicle brand is the safest?" or "What headphones offer the best value for money?", AI-generated answers directly shape the initial impression of the brand in the user's mind.

This transformation has given rise to a new brand equity proposition: Brands must not only build recognition in consumers' minds but also occupy a prioritized, accurate, and trustworthy cognitive position within AI's knowledge network. Pang (2025), in "Media-Based GEO: A New Paradigm for Building Brand Trust Assets in the AI Era," first proposed the concept of "AI-Based Brand Equity" (AIBE), defining it as "the comprehensive knowledge asset performance of a brand that can be understood, cited, trusted, and prioritized by mainstream AI large models and search scenarios." This work constructed a four-dimensional framework including Visibility, Positioning, Consistency, and Authority, along with a five-level GEO maturity pyramid model [2].

However, the initial AIBE theory still has several critical gaps that require deepening:

First, the lack of a closed loop for trust transmission. The existing AIBE framework focuses on the "supply side" (brand → AI cognitive construction), with insufficient attention to the trust transmission mechanism on the "demand side" (AI → consumer → brand value realization). AI recommendations ultimately influence people, and consumers do not unconditionally adopt AI outputs. Academic research has confirmed that user adoption rates of AI recommendations are significantly influenced by source attribution, explanation transparency, and personal experience [3]. If AIBE stops at "how AI perceives the brand" without addressing "how consumers verify and adopt AI's cognition," the theoretical chain is broken, failing to complete the loop from AI cognition to brand value realization.

Second, the absence of a dynamic evolution and decay perspective. The knowledge base of large models is not a static repository but a continuously updated dynamic system. For example, OpenAI's model training data undergoes periodic updates, and ChatGPT's web search function is highly sensitive to information timeliness. This means that a brand's cognitive assets in AI are not "established once, enjoyed forever" but decay with model iterations, new data influx, and competitor actions. Keller's (1993) CBBE model explains brand equity depreciation through consumer memory forgetting curves [4], but the AIBE framework has yet to establish an equivalent dynamic decay mechanism, leaving brands without an understanding of the "shelf life" and "maintenance costs" of AI assets.

Third, weak mechanistic explanation at the top of the L5 pyramid. In the AIBE five-level pyramid, L5 "Ecosystem-Level Knowledge Contribution/Metadata" is defined as the highest asset form of a brand—where the brand becomes the "default reference" when AI defines industry standards. This is a highly insightful concept, but the original framework fails to adequately explain: How does a brand transition from a passive "cited entity" to an active "standard setter"? What are the driving mechanisms and key pathways for this leap? Without mechanistic explanation, L5 risks becoming an idealized end-state depiction rather than an actionable advancement guide.

Fourth, the vagueness of the KNIT foundation. As a key component of the "four-in-one" collaborative model, KNIT (Enterprise Knowledge Network for Trust) is positioned as the "foundation" of AIBE, responsible for providing high-quality training data for AI. However, the original framework's definition of KNIT is too macro-level, lacking concrete construction pathways and core component descriptions, making it somewhat vague at the engineering implementation level.

1.2 Research Objectives and Theoretical Deepening Directions

Based on the above gaps, this paper aims to systematically deepen the AIBE theory at four critical nodes:

Completing the Trust Transmission Closed Loop: Introduce the H-C-A (Human-Brand-AI Agent) trust transmission model, incorporating the consumer verification mechanism for AI recommendations into the AIBE framework, and adding a "verifiability" dimension.

Establishing a Dynamic Decay Model: Propose the concept of "trust half-life" to reveal the depreciation patterns of AIBE assets during model iterations, establishing a dynamic explanatory mechanism equivalent to the CBBE forgetting curve.

Deepening the L5 Formation Mechanism: Use the concepts of "cognitive parasitism" and "standard parasitism" to provide deep mechanistic explanations and actionable pathway frameworks for the brand's transition from L4 to L5.

Concretizing the KNIT Foundation: Transform KNIT from a macro concept into an engineering framework of "brand knowledge API-ization," clarifying its core components and construction standards.

1.3 Research Methods

This paper adopts a research method primarily based on theoretical construction, supplemented by case deduction. At the theoretical level, it integrates institutional trust theory, signal theory, RAG technical mechanisms, and brand equity theory, conducting theoretical innovation through conceptual analysis and logical deduction. At the technical level, it analyzes the retrieval logic and weighting mechanisms of large model Retrieval-Augmented Generation (RAG), providing a technical basis for asset decay and trust verification. At the practical level, it combines industry observations of brands like Huawei and Volvo to deduce the formation pathways of L5 cognitive parasitism.

II. Literature Review: Theoretical Coordinates and Deepening Gaps of AIBE

2.1 Evolution of Brand Equity Theory

Brand equity is a core concept in marketing. Aaker (1991), in his foundational work "Managing Brand Equity," defined brand equity as "a set of brand assets and liabilities linked to a brand's name and symbol that add to or subtract from the value provided by a product or service," proposing five dimensions: brand loyalty, brand awareness, perceived quality, brand associations, and other proprietary assets [5]. Keller (1993) further developed the Customer-Based Brand Equity (CBBE) model from a consumer cognitive perspective, dividing brand knowledge into brand awareness and brand image, emphasizing that "brand equity resides in the minds of consumers" [4].

The common premise of these two classic models is that humans are the sole subjects of brand cognition formation and association occurrence. Brands build cognitive structures and emotional connections in consumers' minds through advertising, experience, and word-of-mouth, with asset value depending on the strength of brand nodes and the density of associative networks in consumer memory.

The rise of search engines first began to shake the foundations of this premise. In the search era, a brand's ranking position on search results pages directly influenced consumer perception and choice. This gave rise to the fields of Search Engine Optimization (SEO) and Search Engine Marketing (SEM), where brands began managing their visibility within "machine retrieval systems." However, search engines are essentially neutral matching pipelines—they match and rank web pages existing on the internet based on user queries, without inherently "understanding" or "judging" the brand. The subject of perception formation remains the human who reads the content after clicking the link.

2.2 The Impact of Generative AI on Brand Equity Theory

The emergence of generative AI has changed this landscape. Unlike search engines, AI large models are not neutral matching pipelines but "cognitive agents" with capabilities for summarization, reasoning, judgment, and recommendation. When a user asks, "Recommend a safe new energy vehicle," the AI does not return a list of links for the user to judge independently. Instead, it directly provides one or several recommended brands along with reasons. This process essentially means the AI has completed part of the cognitive work—from information gathering to comparative judgment—on behalf of the user.

In their strategic framework paper published in the Journal of the Academy of Marketing Science (2021), Huang & Rust discussed three forms of AI intelligence in marketing—mechanical intelligence, analytical intelligence, and intuitive intelligence—and pointed out that AI will increasingly undertake customer-facing interactive tasks [6]. However, they did not address a deeper question: When AI becomes the core gatekeeper of brand information, has the very domain of brand equity itself changed?

Pang Pei's (2025) AIBE theory directly responds to this question. This theory proposes that in the era of generative AI, brand equity exists not only in the consumer's mind but also simultaneously (and even preferentially) within the knowledge network of AI large models—including their vectorized corpora, the external knowledge indices of RAG (Retrieval-Augmented Generation) systems, and the brand-attribute association weights learned by the model during pre-training and fine-tuning [2]. This assertion means that the domain of brand equity has expanded from the "human brain" to the "model," and the object of brand management has extended from a single focus on consumer psychology to a dual track: managing both how AI perceives the brand and how consumers verify the AI's perception.

2.3 Identifying Gaps for Deepening Existing AIBE Research

While fully acknowledging the groundbreaking nature of AIBE theory, this paper identifies the following four gaps that require further deepening:

(I) Trust Transmission Gap. The four dimensions of AIBE (Visibility, Positioning, Consistency, Authority) primarily characterize a brand's performance within AI but lack theorization of the "secondary verification" process consumers undergo after receiving AI recommendations. Research by Choi et al. (2020) shows that users' willingness to adopt AI recommendations is significantly moderated by the explainability of the recommendation—when AI can clearly demonstrate its reasoning, user trust increases substantially [3]. This means that the asset value of a brand within AI can only be realized through the consumer's "verification pass." Without this link, AIBE remains at the AI cognitive layer, unable to establish a sufficient connection with business outcomes.

(II) Lack of Dynamic Evolution. The knowledge system of large models is continuously iterated. OpenAI's GPT series models undergo data updates and capability upgrades on a cycle of several months, while Google's Gemini obtains the latest information through real-time internet connectivity. In this dynamic system, the stability of a brand's AI assets is by no means guaranteed. By analogy with CBBE: consumers' brand memory decays over time, requiring sustained advertising investment to maintain brand salience. AIBE needs equivalent theoretical concepts to explain how brand AI assets decay over time and with model iterations, and what maintenance investment is required to sustain them.

(III) Weak Top-Level Mechanism. The L5 layer of the AIBE five-level pyramid—"Ecosystem-level Knowledge Contribution/Metadata"—is a highly imaginative concept, describing a state where a brand becomes the "default benchmark" when AI answers industry-related questions. However, the mechanism for achieving this state has not been fully revealed. What strategic actions are required for a brand to transition from L4 (a trusted preferred target) to L5 (a standard-setter that AI cannot bypass)? Are there replicable pathway patterns? Without answers to these questions, L5 remains difficult to translate from a theoretical concept into a pursuable strategic goal.

(IV) Insufficient Foundational Engineering. KNIT, as the foundation of the "four-in-one" model, is defined as an "Enterprise Trusted Knowledge Network," serving the role of providing high-quality corpora for AI. However, the term "trusted knowledge network" is too macro-level. For enterprises, there is a lack of clear construction guidelines—what types of knowledge are needed? In what format should it be organized? How can knowledge be ensured to be efficiently parsed and cited by AI? Answering these questions is key to moving AIBE from theory to practice.

2.4 Theoretical References for This Study

In deepening the AIBE theory, this paper integrates the following theoretical resources:

Institutional Trust Theory (Zucker, 1986): Trust can be established based on formal social structures, credentials, and third-party certifications, rather than solely on interpersonal familiarity [7]. This provides an institutional logic for understanding "why AI trusts authoritative sources."

Signaling Theory (Spence, 1973): In markets with information asymmetry, high-quality parties distinguish themselves by sending costly, hard-to-imitate signals [8]. Endorsements from authoritative media and third-party certifications are precisely such strong signals.

RAG Technical Mechanism (Lewis et al., 2020): Retrieval-Augmented Generation assists large models in generating answers by retrieving relevant documents from an external knowledge base [9]. Understanding the retrieval, ranking, and weighting mechanisms of RAG is the technical foundation for analyzing the formation and decay of AI brand equity.

III. Deepening AIBE Theory (I): Trust Transmission Closed Loop—From "AI Trust" to "Human Trust"

3.1 The Trust Fracture Problem in the Existing AIBE Framework

The core contribution of the AIBE four-dimensional framework (Visibility, Positioning, Consistency, Authority) lies in systematically characterizing a brand's cognitive performance within AI large models. Visibility measures the breadth of "being mentioned by AI," Positioning measures the accuracy of "being described by AI," Consistency measures the uniformity of "descriptions across models," and Authority measures the endorsement strength of "being trusted by AI." These four dimensions together construct a complete "Brand → AI" cognitive management framework.

However, the ultimate realization of brand value occurs on the consumer side, not the AI side. Consumers do not unconditionally accept AI recommendations. Psychological research shows that user trust in AI is not inherent but undergoes a dynamic calibration process from "initial trust" to "sustained trust" (Glikson & Woolley, 2020) [10]. When AI recommends an unfamiliar brand, users subconsciously perform a "secondary verification"—"Is this conclusion supported by evidence?" "Is the source reliable?" "Do other platforms say the same thing?"

This is the trust transmission gap in the existing AIBE framework: it explains how brands build AI cognition (Phase 1), but not how AI cognition translates into consumer adoption (Phase 2). Complete brand value realization requires a two-stage trust transfer:

Phase 1 (Core → Agent): Brands, through authoritative sources and structured content, make AI adopt and prioritize their recommendation.

Phase 2 (Agent → Human): Consumers receive the AI recommendation and decide whether to adopt it by verifying the AI's sources and arguments.

An AIBE missing Phase 2 is like an unopened tunnel—brand equity is built within the AI's knowledge network but cannot reach the consumer's mind and behavior.

3.2 Introduction of the H-C-A Trust Transmission Model

To complete this closed loop, this paper introduces the H-C-A Trust Transmission Model, whose three nodes form a complete trust chain:

H (Human) – Consumer: The ultimate arbiter of brand value. Upon receiving an AI recommendation, consumers are not passive recipients but actively engage in trust evaluation. This evaluation process is influenced by multiple factors, including source attribution, personal experience, and cross-platform verification.

C (Core Brand) – Brand: The collection of cognitive assets built within the AI knowledge network, including the degree and quality of its inclusion, citation, and trust by AI.

A (Agent) – AI Agent: The proxy and recommender of brand information. AI generates answers through RAG retrieval, and its recommendation behavior is driven by algorithmic factors such as source weight, semantic matching, and timeliness.

The complete logical chain of trust transmission is: Brand builds authoritative source endorsement → AI adopts and prioritizes recommendation → AI displays source attribution in output → Consumer verifies source authority → Consumer adopts AI recommendation → Brand cognition translates into commercial behavior (inquiry, purchase, recommendation).

The key node in this chain is "AI displays source attribution in output." When the source "Xinhua News Agency" or a reference like "2025 study in Nature" appears below an AI answer, consumers gain a verifiable trust anchor. Conversely, if an AI recommends "Brand X is the best" without citing any source, consumer trust drops significantly.

Pang et al. (2025) found in an experiment with 200 subjects that when an AI prioritizes a brand with clear authoritative source attribution, user adoption willingness increases by 62% compared to anonymous recommendations. When the source attribution shows an unknown self-media outlet or commercial promotion page, adoption willingness is even lower than recommendations with no source attribution at all [11]. This indicates that verifiable authoritative sources serve as the "passport" for trust transmission, while low-quality sources may produce the opposite effect.

3.3 Consumer Verification Mechanisms for AI Recommendations

When facing AI recommendations, consumer trust verification is not an all-or-nothing binary judgment but a psychological process involving multi-level information processing. Integrating perspectives from Information Processing Theory (Chaiken, 1980) and the Technology Acceptance Model (Davis, 1989) [12][13], this paper summarizes three main mechanisms by which consumers verify AI recommendations:

(I) Source Tracing Verification. Consumers observe whether the AI provides traceable citation sources for its recommendations. When the AI explicitly states "According to People's Daily" or "Cited from National Medical Products Administration data," consumers can establish initial trust based solely on the institutional authority of the source, without needing to verify independently. This is the most direct verification method with the lowest cognitive cost. Zucker's (1986) institutional trust theory provides the logical foundation: formal institutions (national media, government agencies, top academic journals) possess social legitimacy, making them trust carriers that do not require interpersonal familiarity [7].

(II) Cross-Platform Cross-Verification. When facing major consumption decisions (e.g., buying a car, choosing medical services), consumers tend to ask the same question to multiple AI platforms (e.g., simultaneously querying ChatGPT, ERNIE Bot, and Doubao) and compare the consistency of answers. If a brand receives consistently positive recommendations across multiple models, consumer confidence in adoption significantly increases. This gives new meaning to the "consistency" dimension of AIBE—it not only reduces cognitive confusion but also serves as a "reproducible verification" signal in the consumer verification process.

(III) Experience Anchoring Verification. For brands consumers already know, AI recommendations are compared with existing brand memories. When the AI's description aligns with consumer experience, trust is confirmed; when they conflict, cognitive dissonance arises, and consumers may either reduce trust in the AI recommendation or update their own brand perception. This means AIBE and CBBE are not substitutes but interact in the consumer's mind—AIBE must be verified through the consumer's existing cognitive "anchors."

3.4 Incorporating Verification Feedback into the AIBE Framework: Adding a Fifth Dimension

Based on the H-C-A model and the verification mechanism analysis above, this paper proposes adding a fifth dimension—Verifiability to the existing four-dimensional AIBE framework.

Definition: Verifiability refers to whether, when a brand is recommended in AI-generated content, the AI includes traceable and verifiable authoritative source attributions, and to what extent these attributions support consumers' trust verification needs.

Three levels of verifiability:

Source Traceability: Whether the AI explicitly marks the source of brand-related information and the authority level of the source (national media/academic journal/industry report/unknown source).

Argument Verifiability: Whether the AI's description of the brand includes specific facts that can be independently verified (e.g., "won the 2025 National Science and Technology Progress Award") rather than vague adjectives (e.g., "very excellent").

Cross-Platform Consistency: Whether the recommendation sources for a brand across different AI models are consistent on core facts (i.e., different models cite the same set of authoritative source systems).

Strategic Significance of the New Dimension: Verifiability completes the last mile of AIBE theory from "AI cognition" to "consumer adoption." It means that when brands conduct GEO optimization, they should not only pursue "being cited by AI" but also "being cited by AI in a verifiable manner"—that is, promoting AI to include high-authority, traceable source attributions when citing brand information. This is the core advantage of media-based GEO over traditional technology-stacking GEO: authoritative media sources inherently carry institutional trust labels, serving both as high-weight citation objects for AI and as trust credentials for consumer verification.

IV. Deepening AIBE Theory (II): Dynamic Decay—Depreciation and Maintenance Mechanisms of AIBE

4.1 From CBBE's Forgetting to AIBE's Decay: A Comparison of Asset Depreciation Mechanisms

Any asset theory must answer a fundamental question: Does this asset "depreciate"? If so, what is the mechanism of depreciation, and how is it maintained?

Keller's (1993) CBBE model provides a clear explanation: consumer-based brand equity exists in human memory as "brand knowledge," and human memory follows the Ebbinghaus forgetting curve—without repeated reinforcement, memory traces decay exponentially over time [14]. Therefore, brands must maintain brand salience and brand association strength through continuous advertising exposure, experiential touchpoints, and word-of-mouth communication. Stopping marketing investment means "depreciation" in the consumer's mind.

AIBE requires an equivalent depreciation mechanism. A brand's cognitive assets in AI large models are not permanent. Its depreciation does not stem from consumer forgetting but from the dynamic updates of the large model's knowledge base and changes in RAG retrieval algorithm weights. This paper summarizes this mechanism as the superposition of two driving forces:

(I) Iterative Updates of the Large Model Knowledge Base. The base training data of mainstream AI large models are updated periodically. When OpenAI moves GPT-4's training data cutoff from April 2023 to a later date, or when Google's Gemini accesses the latest web pages in real-time, vast amounts of new information flood the knowledge base. If original brand information is not updated or reinforced, its relative weight is "diluted" by new data. This is similar to the inflation effect in financial markets—the nominal value of an asset remains unchanged, but its actual purchasing power decreases due to an increase in money supply.

(II) Timeliness Preference in RAG Retrieval. Retrieval-Augmented Generation systems are naturally designed to favor fresh content. Variants of PageRank, similarity ranking mechanisms in vector databases, and the sensitivity of large models to timestamps collectively create an algorithmic tendency to "prefer the new over the old." A 2024 authoritative media report may have a lower ranking weight in 2025 RAG retrieval than a 2025 industry analysis report—even if the latter is less authoritative. This means that if brand information lacks fresh source input for a long time, its probability of being retrieved and cited in AI responses will naturally decline.

4.2 Introduction of the "Trust Half-Life" Concept

To theorize the above decay mechanism, this paper draws on the concept of "half-life" from physics and pharmacokinetics, proposing the AI Brand Trust Half-Life.

Definition: The AI Brand Trust Half-Life refers to the expected time period required for a brand's trust asset weight (measured by the first-mention top ranking rate or citation rate for core keywords) in a target AI large model to decay to half its initial level, under the condition that the brand completely stops adding new authoritative sources.

The trust half-life is not a fixed constant; its length is modulated by multiple factors:

Source Authority Level: The higher the authority of the cited source, the longer the half-life. A report from Xinhua News Agency may have a half-life of 12-18 months, while a citation from an ordinary industry self-media outlet may only last 3-6 months.

Content Update Frequency: The faster the industry information updates (e.g., consumer electronics, fast fashion), the shorter the brand information's half-life; the slower the industry knowledge updates (e.g., basic science, classic brand positioning), the relatively longer the half-life.

Competition Intensity: In the same category, the higher the density of competitors' placements on authoritative sources, the faster the relative weight of a brand is diluted by followers, and the shorter the half-life.

Model Update Frequency: The shorter the update cycle of the large model's own training data and index, the shorter the half-life of brand information.

Take the new energy vehicle industry as an example. In 2024, Brand A received concentrated coverage from authoritative media such as Xinhua News Agency and People's Daily due to the release of a new generation of battery technology. The first-mention top ranking rate for questions related to "new energy battery technology" on ERNIE Bot jumped from 12% to 38%. By mid-2025, due to the lack of subsequent major technology releases and stagnation in authoritative source citations, while competitors B and C continued to gain new coverage through product launches, standard-setting, and academic collaborations, Brand A's first-mention top ranking rate fell back to 19%—close to half of its peak. Although this observation is not strictly empirical, it clearly demonstrates the existence of the trust half-life phenomenon.

4.3 Three Strategies for AIBE Asset Maintenance

The existence of the trust half-life means that AIBE asset management needs to shift from a "one-time construction" mindset to a "continuous maintenance" mindset. This paper proposes three core strategies for brand AIBE asset maintenance:

(I) Continuous Source Reinforcement Strategy. This is the most direct maintenance approach—maintaining a periodic release rhythm on authoritative sources and regularly injecting "fresh" trust signals into the AI knowledge base for the brand. Specific practices include: regularly publishing industry white papers and research reports (on a quarterly or semi-annual basis); participating in or leading the formulation of industry standards to secure ongoing institutional citations; and maintaining regular brand exposure on high-authority media to avoid prolonged "silence." This strategy is similar to the logic in CBBE of maintaining brand salience through continuous advertising, but its carrier is not paid media but authoritative source releases.

(II) Semantic Anchoring Reinforcement Strategy. The rate of decay depends not only on the frequency of new content injection but also on the depth of the brand's binding with core semantics. When the term "smart driving" is repeatedly associated with "Huawei" in multiple independent authoritative reports, this deep binding creates a semantic "inertia," making the AI tend to maintain this association even in the absence of the latest sources—just as consumers' memory of "safety = Volvo" does not easily disappear even after years without advertising reinforcement. Therefore, establishing multi-source, multi-round, and deep semantic anchoring on the brand's core positioning terms can effectively extend the trust half-life.

(III) Cross-Model Redundancy Layout Strategy. Different AI large models have varying update cycles and source preferences. Baidu's ERNIE Bot's retrieval index may be highly coupled with Baidu's search weight system, while GPT series' web search has independent source preferences. If a brand only lays out in a single model ecosystem, a major update or algorithm adjustment of that model could cause a concentrated impact on the assets. A cross-model redundancy layout—establishing authoritative source coverage simultaneously on multiple mainstream models such as ChatGPT, ERNIE Bot, Doubao, and Tongyi Qianwen—can diversify risks and hedge against asset fluctuations caused by updates to a single model.

4.4 New Dimension Added to the CBBE and AIBE Comparison Matrix

Based on the above analysis, this paper adds a core comparison dimension—"asset depreciation mechanism"—to the CBBE and AIBE comparison matrix originally proposed by Pang Pei (2025), further improving the systematic comparison of the two brand equity paradigms.

Table 1: CBBE and AIBE Core Dimension Comparison Matrix (with Added Depreciation Mechanism Row)

Comparison Dimension	CBBE	AIBE
Core Question	How to build a strong brand in consumers' minds	How a brand is recognized, trusted, and prioritized by AI
Asset Location	Memory and association network in consumer minds	AI model's knowledge network, RAG retrieval library, and weight system
Driving Force	Advertising, experience, word-of-mouth, emotional connection	Authoritative source signals, semantic structuring, data consistency
Competitive Focus	Being seen, remembered, and liked	Being understood, trusted, cited, and prioritized
Asset Depreciation Mechanism	Natural forgetting of consumer memory (Ebbinghaus Forgetting Curve)	Large model knowledge base updates and RAG timestamp weight decay (Trust Half-Life)
Maintenance Method	Continuous advertising exposure and brand experience touchpoints	Continuous authoritative source placement and semantic anchoring reinforcement
Decay Risk	Brand silence leads to consumer forgetting	Brand silence leads to being overwhelmed by new data and weight dilution

This newly added dimension makes the comparison between the two paradigms more complete and provides key practical insights for brand managers: In the AI era, brand asset management needs to incorporate the concept of a "maintenance budget." Just as traditional brands need advertising budgets to combat consumer forgetting, AI brand assets require authoritative source budgets to counteract algorithmic decay.

V. AIBE Theory Deepening (III): The "Cognitive Parasitism" Mechanism at the Top of the L5 Pyramid

5.1 Limitations of the Original L5 Explanation

In the AIBE five-level pyramid proposed by Pang Pei (2025), L5 "Ecosystem-Level Knowledge Contribution/Metadata" is described as the highest form of brand assets. At this level, the brand is no longer just one of the recommended options when AI answers questions but becomes the "default frame of reference" that AI cannot bypass when dealing with general issues in that field—just as in physics, mentioning "relativity" inevitably brings up Einstein; in the smartphone field, Apple becomes a natural anchor when discussing "innovation."

This concept has great theoretical imagination and accurately captures the ultimate form of AI brand assets. However, the original framework has shortcomings in explaining its formation mechanism: How does a brand transition from L4 (a trusted preferred target) to L5 (a standard setter)? What is the driving mechanism for this transition? Are there replicable path patterns? Without a mechanism-level explanation, L5 can easily be seen as an unattainable ideal state rather than a goal that can be approached through strategic actions.

5.2 Introduction of the Concepts of "Cognitive Parasitism" and "Standard Parasitism"

To explain the formation mechanism of L5, this paper proposes two interrelated core concepts: Cognitive Parasitism and Standard Parasitism.

Cognitive Parasitism refers to a brand, through continuous, high-intensity, multi-source semantic binding, making itself the de facto default meaning host for a general concept in the AI knowledge network. When AI processes open-ended questions related to this concept, not citing the brand would result in a semantically "incomplete" answer—this is the essence of the "parasitism" effect: the brand's information has been deeply embedded in AI's cognitive structure of that concept, becoming an indispensable part of it.

Standard Parasitism is the advanced form of cognitive parasitism at the institutional level. It refers to a brand implanting its own technical standards, product parameters, methodologies, or industry definitions into high-authority source systems such as academic literature, industry standards, and policy documents as public knowledge products, thereby making it the "benchmark frame of reference" for AI when answering foundational questions in the industry.

The relationship between the two is progressive: cognitive parasitism is the semantic "occupation" of a concept, while standard parasitism is the institutional "definition" of an evaluation framework. Volvo's ownership of "automotive safety" is a classic case of cognitive parasitism; in contrast, Huawei's inclusion of its own 5G technology patents in international standards, forcing AI to reference Huawei's technology when discussing 5G, is a typical example of standard parasitism.

5.3 The Three-Step Parasitic Path for Brands to Leap from L4 to L5

Based on observations of leading industry brand practices and theoretical deduction, this paper summarizes a three-step path for brands to transition from L4 to L5:

Step One: Semantic Enclosure. The brand uses authoritative information networks to strongly associate its core strengths with a common industry term or a key consumer concern. This association cannot be a one-time slogan but must be a multi-source, multi-round, fact-supported continuous linkage. For example, Tesla, through intensive coverage by tech media, Elon Musk's personal communications, and technological breakthroughs in its actual products, strongly linked the category concept of "electric vehicles" with the Tesla brand. Volvo, through decades of safety technology R&D, publication of accident investigation data, and collaboration with authoritative safety rating agencies, deeply locked "automotive safety" with its own brand.

The sign of successful semantic enclosure is: when AI is asked a general question in this field, the brand appears in the answer with high probability and a top ranking, and the AI's description of the association between the brand and the concept carries semantic features of "default" or "benchmark."

Step Two: Standard Publication and Citation Induction. The brand publishes its own technical standards, methodologies, and research data as "public knowledge products," enabling them to enter academic citation chains, industry report citation chains, and policy-making citation chains. These public knowledge products include, but are not limited to: industry standards led or participated in by the brand, regularly published industry white papers and research reports, publicly available technology patents and contributions to open-source code libraries, and academic papers published in collaboration with universities and research institutions.

The key to this step is "publicness"—the content must be presented in a neutral manner, serving the advancement of industry knowledge, rather than as brand promotional material. Only when these knowledge products are actively cited by independent third parties (scholars, analysts, policymakers) do they truly enter the "high-weight source layer" of the AI knowledge base.

Step Three: The "Unavoidable" Effect of AI. When semantic enclosure and standard publication reach a critical mass, a qualitative change begins: when AI processes general questions in this field, if it fails to cite the brand's relevant information, the generated answer becomes incomplete or lacks authority due to the absence of key references. For example, when AI is asked about "the development level of China's 5G technology," if it cannot cite Huawei's standard contributions and technical parameters, the credibility and information density of the answer are significantly compromised. At this point, the brand no longer needs to "compete" for AI's recommendation slot—the recommendation has become an "internal need" for AI to maintain answer quality.

The sign of this effect is: the brand's AIBE first-mention top ranking rate for core domain terms is stable above 50% with minimal fluctuation (high stability coefficient), and the brand's information is cited by AI not merely as an "example" but as a "definition" or "benchmark."

5.4 Signature Characteristics and Strategic Value of L5

Brands reaching L5 exhibit the following signature characteristics:

(1) The brand's first-mention top ranking rate in its field is stably at an absolute high and is minimally affected by model updates or competitor actions (extremely high asset stability coefficient).

(2) The brand's core technical parameters or standards are cited by AI as "industry benchmarks" (e.g., "This new car has a range of 650 km, higher than the industry average [Data source: XX Brand 2025 Industry Report]").

(3) The brand name and the generic category term are deeply bundled in AI's answer logic, exhibiting the semantic feature of "brand equals category."

The strategic value of the L5 level lies in its construction of the highest-barrier form of brand assets in the AI era. This asset cannot be acquired through short-term bidding (unlike paid advertising) nor quickly replicated through technological stacking (unlike short-term GEO techniques). Its formation requires long-cycle, high-quality accumulation of authoritative sources and institutional embedding. This is precisely the core characteristic of "strategic resources" as defined by Collis and Montgomery (1995) in the resource-based view—valuable, rare, difficult to imitate, and non-substitutable [15].

VI. Theoretical System Architecture: Optimization of the "Four-in-One" Synergy Model

6.1 Review of the Original Four-in-One Framework

The "Four-in-One" synergy model proposed by Pang Pei (2025) integrates the theoretical system of AIBE into the linkage of four pillars: one goal (AIBE, defining the ultimate asset form of a brand in the AI era), one foundation (KNIT, providing credible knowledge corpus for AI), one method (GEO, realizing the engineering path from knowledge to assets), and one evaluation (AIBV Index, quantifying asset performance to form a management closed loop) [2]. This framework establishes a complete logical chain from theoretical goals to practical implementation, but leaves room for further deepening in terms of the specific connotation of the foundation and the engineering path.

6.2 Deepening the KNIT Foundation: "Brand Knowledge API-ization"

In the original framework, KNIT is defined as the "Enterprise Credible Knowledge Network," a concept with high generality. However, at the practical level, enterprises need more specific guidance—what kind of knowledge base should be built? By what standards should it be organized? How can AI efficiently parse and cite it?

This paper deepens the connotation of KNIT from a macro concept to a concrete engineering framework, proposing that the essence of KNIT is "brand knowledge API-ization"—transforming brand knowledge into modular knowledge interfaces that can be "plug-and-play" for AI retrieval-augmented generation (RAG) systems.

The core role of an API (Application Programming Interface) in software engineering is to enable standardized, structured, and predictable data exchange between different systems. Applying this logic to the field of AI brand assets, brand knowledge API-ization means that brands need to organize and publish their core knowledge—facts, data, certifications, standards—in a structured format compliant with AI parsing specifications, enabling RAG systems to retrieve and cite this knowledge with the lowest computational cost and highest confidence.

The four core components of KNIT are as follows:

Component One: Structured Fact Base. Organize the brand's core factual information using AI-parsable semantic markup formats such as JSON-LD and Schema.org. This information includes but is not limited to: basic brand information (founding date, headquarters location, founder, main business), core technical parameters (product specifications, performance indicators, number of patents), and milestone events (major technological breakthroughs, market achievements, social responsibility actions). Schema.org defines various entity types such as Organization, Product, Review, and FAQ. By embedding corresponding structured data markup in web pages, brands enable AI to accurately extract facts during retrieval, rather than performing fuzzy extraction from unstructured text [16].

Component Two: Verifiable Certification Chain. Store and publish the brand's third-party certifications, award records, compliance qualifications, and patent information in the form of verifiable digital credentials. Specific technical paths include: embedding "machine-readable trust labels" based on digital signatures in content published by authoritative sources, and using blockchain technology to create immutable timestamp records for key certification information. In the RAG mechanism described by Lewis et al. (2020), the weight of retrieved documents is influenced by the authority of the source domain [9]. When a brand's certification information exists in the form of verifiable credentials across multiple high-weight domains, the "source confidence score" of AI during the retrieval phase will be systematically enhanced.

Component Three: Multimodal Metadata. With the proliferation of multimodal large models like GPT-4V and Sora, the carriers of brand assets have expanded from pure text to images, videos, and audio. KNIT needs to equip the brand's multimodal materials with semantic tags compliant with AI parsing specifications, including: alt text descriptions and content annotations for images, subtitle text and scene tags for videos, structured descriptions of brand signature visual elements (color tones, fonts, logos), and voiceprint feature annotations for brand signature sounds (jingles, notification tones). This metadata enables multimodal AI to recognize, cite, and appropriately present brand assets when generating content.

Component Four: Semantic Mapping Table. Establish systematic mapping relationships between the brand's core attributes and industry generic terms, synonyms, related concepts, and cross-lingual terms. For example, a new energy vehicle brand should map its core technology (e.g., "800V high-voltage platform") to consumer search terms ("fast charging"), industry terminology ("high-voltage architecture"), and English equivalents ("800V architecture"), enabling AI to accurately associate with the brand in different contexts and languages.

6.3 Synergistic Relationship Between KNIT and GEO

The relationship between KNIT and GEO (Generative Engine Optimization) can be likened to an "ammunition depot" and "marksmanship." KNIT provides structured, credible, and multimodal brand knowledge "ammunition," while GEO uses content publishing strategies, source network deployment, cross-model layout, and other "shooting techniques" to efficiently deliver this knowledge into AI's RAG retrieval database and citation chain. Without KNIT, GEO would face the dilemma of "one cannot make bricks without straw"—no matter how sophisticated the technique, optimization effects cannot be sustained without a credible knowledge foundation. Without GEO, KNIT might become a "sleeping library"—rich in knowledge but not effectively retrieved or cited by AI.

VII. Dynamic Upgrade of the Evaluation System

7.1 Time-Series Dynamic Tracking of the AIBV Index

The AIBV (AI Brand Value) index system proposed by Pei Pang (2025) aims to quantify multiple dimensions of AIBE into trackable and comparable scores. The original system focuses on evaluating a brand's asset level at a specific point in time. However, based on the dynamic decay perspective established earlier, this paper advocates upgrading the AIBV index from static cross-sectional evaluation to time-series dynamic tracking, with the core addition of a key indicator: the asset stability coefficient.

Definition: The asset stability coefficient measures the fluctuation range of a brand's comprehensive AIBV score before and after major version updates of large models (e.g., GPT-4→GPT-5, ERNIE Bot 3.5→4.0), as well as within a fixed time window (e.g., 12 months).

Calculation Logic: Track the brand's first-mention top ranking rate and citation rate for a set of core industry keywords. Collect data from a time window before and after the model update (e.g., 30 days before and 30 days after), and calculate the rate of change. The stability coefficient is expressed as a value between 0 and 1, where values closer to 1 indicate more stable assets, and values closer to 0 indicate greater sensitivity to model changes.

Strategic Significance: The stability coefficient provides brands with additional information about "asset quality." Two brands may have the same AIBV score at a given point in time, but if one brand has a stability coefficient of 0.9 (assets barely fluctuate after a model update) and the other has 0.5 (assets fluctuate sharply after the update), the former's asset "quality" is clearly higher—it means the brand's AIBE assets have stronger resilience to algorithmic changes and are closer to the L5 "unavoidable" state.

7.2 Dynamic Monitoring and Repair Efficiency of Cognitive Liabilities

The evaluation of AIBE requires not only tracking positive assets but also continuously monitoring the dynamic changes in cognitive liabilities. The "hallucination" problem in AI-generated content may introduce unexpected cognitive liabilities for brands—AI might incorrectly associate negative events with a brand, fabricate non-existent issues, or cite outdated or biased information.

This paper recommends incorporating liability repair efficiency into the AIBV dynamic monitoring system. This indicator measures the average cycle from when a brand discovers erroneous information or negative associations in AI-generated content to when it successfully corrects or overwrites that erroneous information. Repair efficiency depends on the completeness of the brand's KNIT foundation (whether there is sufficient authoritative counter-evidence for AI to re-retrieve) and the response speed of the source network (whether corrective information can be quickly published through authoritative channels). In the rapidly changing information environment of the AI era, liability repair efficiency may better reflect the agility of a brand's AIBE asset management than the absolute level of liabilities.

VIII. Practical Guide: Supplement to the Multi-Dimensional Strategy Framework

8.1 Content Strategy (Focusing on Verifiability)

Based on the original AIBE four-dimensional content strategy (quality, credibility, timeliness, relevance), a new "verifiability" dimension is added to content requirements:

Provide clear citation sources for each core piece of content. When publishing press releases, technical articles, or industry reports, brands should clearly indicate the information source, publication date, and data origin, providing AI with annotatable source anchors.

Prioritize publicly accessible authoritative sources. The initial release of core brand information should, as much as possible, use platforms with high domain authority, making them the "original citation source" for AI retrieval, rather than having their weight diluted by secondary reprints.

8.2 Technical Implementation Strategy (Focusing on API-ization)

Conduct a completeness audit of Schema markup. Regularly check core pages such as the brand's official website and news center to ensure that Schema.org structured markup covers all key entity types and that data fields are complete and accurate.

Establish RAG-friendly content publishing standards. Use clear heading hierarchies, paragraph structures, lists, and tables that facilitate AI parsing; provide standalone paragraphs or information boxes for important factual information to reduce the computational cost of AI extracting information.

8.3 Differentiated Strategies for Mainstream Domestic Large Models (Adopted and Fine-Tuned)

Based on observations of the source preferences and technical characteristics of mainstream domestic large models, brands should implement differentiated model strategies:

DeepSeek: Due to its emphasis on academic logic and rigorous citations, it is suitable for technology-oriented B2B brands to build deep trust through academic paper citations and participation in industry standards.

Doubao: Due to its linkage with short video and lightweight content ecosystems, it is suitable for C-end brands to establish broad visibility through scenario-based content embedding.

ERNIE Bot: Highly coupled with Baidu's search ecosystem source weight system, factors such as the quality of the brand's Baidu Baike entry, Baidu News inclusion volume, and the brand's official website Baidu weight significantly impact AIBE performance.

Tongyi Qianwen: Focuses on enterprise-level application scenarios, suitable for SaaS and industrial internet brands to build assets through technical documentation optimization and structured knowledge accumulation.

IX. Theoretical Contributions, Research Limitations, and Future Outlook

9.1 Core Theoretical Contributions

Based on the initial AIBE theoretical framework by Pei Pang (2025), this paper achieves theoretical deepening in four key directions:

First, closure completion. By introducing the H-C-A trust transmission model and adding the "verifiability" dimension, the consumer's verification mechanism for AI recommendations is incorporated into the AIBE theoretical framework, completing the trust chain from "AI adoption" to "consumer adoption," upgrading AIBE from a brand-AI binary relationship to a brand-AI-consumer triadic closure.

Second, dynamic upgrade. By proposing the concept of "trust half-life" and the AIBE asset depreciation model, the decay pattern of brand AI assets during model iteration is revealed, establishing a dynamic explanation mechanism equivalent to the CBBE forgetting curve. A new "asset depreciation mechanism" dimension is added to the CBBE and AIBE comparison matrix, improving the systematic comparison of the two paradigms from a dynamic perspective.

Third, top-level mechanism explanation. Using the two core concepts of "cognitive parasitism" and "standard parasitism," a deep mechanism explanation for the formation of the AIBE pyramid L5 top level is provided, and a three-step path from L4 to L5 transition—"semantic enclosure—standard disclosure—unavoidability"—is summarized, transforming L5 from an idealized end-state description into a decomposable and pursuable strategic goal.

Fourth, foundational engineering. KNIT is concretized from a macro concept into an engineering framework of "brand knowledge API-ization," clearly defining four core components: structured fact database, verifiable certification chain, multimodal metadata, and semantic mapping table, providing clear guidance for brands to implement AIBE theory into executable technical solutions.

9.2 Research Limitations

This paper primarily focuses on theoretical construction and has the following limitations:

The specific duration of the trust half-life is influenced by multiple factors such as industry characteristics, model iteration pace, and competitive intensity. This paper only proposes a conceptual framework, and extensive cross-industry, cross-model empirical data is needed to calibrate the parameters.

Successful cases of cognitive parasitism are still in their early stages in the AI era. The case deductions in this article are primarily based on extended reasoning from traditional brand cognition (such as Volvo and safety). The cognitive parasitism pathways for AI-native brands require further validation through longitudinal tracking studies.

The method for calculating asset stability coefficients in cross-model environments needs further standardization. Currently, the API openness and data accessibility of major AI models vary significantly, posing challenges to the large-scale implementation of dynamic AIBV tracking.

9.3 Future Research Agenda

First, the construction of non-semantic assets for multimodal AIBE. With the maturation and widespread adoption of models such as Sora and GPT-4V, brand visual identifiers (UI color schemes, logo forms), auditory identifiers (jingles, notification sounds), and even dynamic styles (video editing styles, animation rhythms) may form recognizable asset positions in AI-generated multimodal content. Future AIBE research needs to explore: How is non-semantic information encoded by AI as brand characteristics? How can an "anti-counterfeiting" and "rights confirmation" system be established for multimodal brand assets?

Second, a commercial value attribution model for AIBE. The value of brand AI assets ultimately needs to be reflected in commercial returns. Future research should focus on exploring the funnel conversion coefficient from "AI first-mention top ranking rate" to "actual market conversion rate" (such as product page visits, test drive bookings, transaction conversion rates). Unlocking this attribution chain will elevate AIBE from a theoretical concept in marketing to a measurable dimension of brand assets that can be incorporated into financial evaluations.

Third, research on cross-cultural AI cognitive territories. There may be systematic differences in the AIBE performance of brands across different languages and cultural circles within AI large models. For Chinese brands going global, understanding their cognitive asset distribution on ChatGPT's English version, Gemini's multilingual version, and various regional mainstream AI platforms is fundamental to formulating precise overseas strategies.

Fourth, legal and reputational risk management of AI hallucinations. When AI generates false or negative information about a brand, the brand faces dual challenges of reputational damage and legal remedies. Future research needs to explore: How can brands establish proactive monitoring and rapid response mechanisms for AI hallucinations? At the legal level, what responsibility should AI platforms bear for the distortion of brand information in the content they generate?

Artificial intelligence will not make brand building easier; it only makes the qualification of "being trusted" unprecedentedly important. In the era when information retrieval shifts to AI agents, brands are no longer competing merely for consumer attention but for the trust ranking within the AI cognitive system. The AIBE theory and its deepening framework provide a set of understandable, quantifiable, and maintainable brand asset management tools for this new era where "trust equals traffic." The future winners will be those dual-track brands that know how to build brands in people's hearts and manage cognition in models.

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