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China Entrepreneur AI Influence Index

CEAI:2026

China Entrepreneur AI Influence Index

China Entrepreneur AI Influence Index

Compiling Institution: World Intelligence Organization (WIO), CNBNTV (China New Vision Internet Television Co., Ltd.)

First Release Date: June 2026

Release Cycle: Monthly

Language Versions: Chinese / English

I. Introduction

1.1 Compilation Background

In an era where generative artificial intelligence is deeply embedded in information acquisition and decision-making processes, the personal image of entrepreneurs is no longer shaped solely by traditional media and social media. When users ask AI large models such as ChatGPT, DeepSeek, Doubao, Ernie Bot, and Gemini, "Who is the most visionary tech entrepreneur in China?" or "Is the founder of this brand trustworthy?", the AI's responses directly constitute the public's "AI first impression" of the entrepreneur.

The trend of integrating an entrepreneur's personal IP with their brand image is increasingly prominent. In the Media-based GEO theory, Pang Pei (2025) points out that AI cognitive authorization not only affects brands but also profoundly impacts the core figures behind them. The image of entrepreneurs in AI-generated content, the topics they are associated with, and the scenarios in which they are recommended have become crucial components of their personal and corporate intangible assets. However, this new form of influence still lacks a systematic quantitative assessment tool.

1.2 Compilation Purpose

The China Entrepreneur AI Influence Index (CEAI) aims to:

• Quantitatively assess the comprehensive influence of Chinese entrepreneurs in content generated by major global AI large models, covering image, associated topics, and recommendation contexts;
• Dynamically track temporal changes in entrepreneurs' personal AI influence, identifying reputational risks and fluctuations;
• Provide integrated brand protection by offering monitoring tools for entrepreneurs and their brand management teams to manage AI cognitive assets, enabling coordinated management of personal and brand perception;
• Enable industry benchmarking by providing a comparable metric for the AI influence of entrepreneurs across different industries and generations.

1.3 Theoretical References

The compilation of this index references the following international standards and academic achievements:

• United Nations Statistics Division, "Handbook on Statistical Indicators" (2015)
• OECD, "Handbook on Constructing Composite Indicators" (2008)
• ISO 10668:2010, "Brand Valuation – Requirements for Monetary Brand Valuation"
• Pang Pei (2025), "Media-based GEO" and "AI Brand Equity (AIBE)" theories
• Pang Pei (2026), "Six-Dimensional Model of AI Influence," particularly the applicability of the "Credibility," "Consistency," and "Stability" dimensions to person evaluation
• Pang Pei (2026), "H-C-A Trust Transmission Model," explaining how consumers verify AI recommendations for brands and individuals

II. Definition and Scope

2.1 Index Definition

The China Entrepreneur AI Influence Index (CEAI) is a composite statistical index that comprehensively measures the image performance, topic association strength, and recommendation context advantage of Chinese entrepreneurs in content generated by major global generative AI large models. It reflects how entrepreneurs are perceived within the AI knowledge system, the topics they are discussed in relation to, the scenarios in which they are recommended, and the accuracy and sentiment of these presentations.

2.2 Core Construct: Entrepreneur AI Influence

Entrepreneur AI Influence comprises three core dimensions:

• Image Power: The overall performance of an entrepreneur's personal image in AI-generated content – including visibility, reputation, descriptive accuracy, and sentiment orientation. It answers, "How does AI describe this entrepreneur? What kind of person does the public learn about him/her through AI?"
• Agenda Association Power: The industry topics, social issues, and ideological systems an entrepreneur is associated with in AI-generated content. It answers, "Which fields and topics is this entrepreneur linked to by AI? Is he/she considered a thought leader in a specific area?"
• Recommendation Context Power: The probability and ranking of an entrepreneur being recommended by AI in response to questions involving "recommend a founder/leader/role model," etc. It answers, "When users seek business leader examples, to what extent does AI recommend this entrepreneur?"

2.3 Coverage Scope

• Entrepreneur Sample: The initial phase includes approximately 100 Chinese entrepreneurs, covering six major industries: technology, manufacturing, consumer goods, finance, pharmaceuticals, and energy. It considers different generations (born from the 1960s to the 1990s) and company types (listed companies, unicorns, specialized and new "little giant" enterprises). Sample selection criteria are detailed in Chapter 5.
• Model Scope: Covers no fewer than 10 mainstream generative AI large models globally:
  • International models: OpenAI GPT-4o/5, Google Gemini 1.5/2.0, Anthropic Claude 3.5/4, Meta Llama 4
  • Chinese models: Baidu ERNIE 4.0, ByteDance Doubao, Alibaba Tongyi Qianwen 2.5, DeepSeek V3/R1
  • Regional models: Naver HyperCLOVA X, Mistral Large
• Language Scope: Chinese and English (mandatory), gradually expanding to Japanese, Korean, Spanish, French, and German.
• Time Span: Base period is the first quarter of 2026, with monthly releases thereafter.

III. Index Architecture and Indicator System

3.1 Index Hierarchy

CEAI adopts a three-level hierarchy:

• Overall Index (CEAI): Reflects the comprehensive level of entrepreneurs' AI influence.
• Sub-indices (3): Image Influence Index, Issue Relevance Index, Recommendation Context Index.
• Basic Indicators (10): Constitute the smallest units of data collection.

3.2 Indicator Framework

Table 1 – CEAI Indicator System

Weight Determination Method: Using the Delphi method, invite no fewer than 15 experts in entrepreneurship research, AI technology, brand communication, and reputation management to conduct three rounds of independent scoring to determine weights. Weights are reviewed annually.

3.3 Score Standardization

Raw values of each basic indicator are mapped to a 0–100 range using Min-Max normalization:

X_normalized = (X_raw - X_min) / (X_max - X_min) × 100

Where Xmin and Xmax are the minimum and maximum values of the indicator among all evaluated entrepreneurs in the current period, respectively.

For cross-period comparison, the first quarter of 2026 is set as the base period, with the CEAI base value set to 100. Subsequent quarters are linked using the chain index method.

IV. Data Collection and Processing Methods

4.1 Questionnaire Design

To comprehensively assess entrepreneurs' AI influence, a multi-dimensional standardized questionnaire is designed:

(I) Image Power Questionnaire (Approximately 50 Questions)

(II) Issue Relevance Questionnaire (Approximately 30 Questions)

(III) Recommendation Context Questionnaire (Approximately 30 Questions)

Each entrepreneur's questionnaire can be personalized based on their industry. The total questionnaire includes no fewer than 110 standard questions, evenly split between Chinese and English, with no more than 20% updated each quarter.

4.2 Data Collection

• Tool: Queries are sent to target AI large models via standardized API interfaces, and complete response texts are automatically recorded.
• Frequency: Data is collected once per quarter. Each question is independently queried 3 times per model (with intervals of no less than 24 hours), and the average of the 3 results is taken to reduce response randomness.
• Data Volume: Single collection volume = 10 models × 110 questions × 2 languages × 3 repetitions = approximately 6,600 response records (per entrepreneur).
• Privacy and Ethics: All collection targets only publicly available information and does not involve personal private data. Entrepreneurs have the right to appeal and correct erroneous information.

4.3 Data Processing

• Named Entity Recognition: A pre-trained RoBERTa-NER model is used to extract entrepreneur names, positions, and company names from response texts. A specialized model is used for Chinese content. Manual spot-checking rate is no less than 10%.
• Sentiment Analysis: For sentence-level texts describing entrepreneurs, a large model is used for three-category sentiment classification (positive/neutral/negative), with dedicated models for Chinese and English.
• Information Accuracy Determination: A standard fact database is established for each entrepreneur (name, birth year, education, current position, major achievements, representative statements). Factual descriptions in AI responses are compared against the standard database to determine accuracy/error/omission.
• Topic Classification: Use a predefined industry topic classification system (e.g., about 30 tags such as "New Energy," "AI Technology," "Globalization," "Social Responsibility") to perform multi-label classification on entrepreneur-related topics in AI responses.
• Thought Leadership Identification: Identify whether AI directly quotes entrepreneurs' views or statements through quotation marks (text within quotes) and attribution sentence patterns (e.g., "XXX believes," "XXX proposes," "according to XXX").
• Consistency Analysis: Compare the semantic similarity of core descriptions of the same entrepreneur across models, using Sentence-BERT to calculate cosine similarity.

4.4 Outlier Handling

• When a single collection result significantly deviates (exceeding 3 standard deviations from the mean), an additional supplementary query is conducted to replace the outlier.
• If a certain AI model experiences a systemic failure and cannot be collected, the weight of that model for the period is temporarily distributed to other similar models, with a public explanation in the report.

V. Entrepreneur Sample Selection

5.1 Sample Selection Principles

• Industry Representation: Cover major economic industries in China, prioritizing founders and actual controllers of leading companies in each industry.
• Generational Diversity: Include different birth decades (1960s, 1970s, 1980s, 1990s) to reflect differences in AI influence among entrepreneurial groups across generations.
• Data Measurability: Ensure entrepreneurs have sufficient information in AI models (richness of public reports) to support data collection needs for standardized question sets.
• Dynamic Adjustment: Support annual adjustments—new entrepreneurial leaders or those with rapidly rising industry influence can be included; those who have stepped back or have insufficient information can be replaced.

5.2 Initial Sample

The initial sample includes approximately 100 Chinese entrepreneurs, with industry distribution as follows:

The full list is provided in Appendix A. The list is nominated by an expert committee and determined through multiple rounds of selection, with annual reviews.

5.3 Dynamic Adjustment Mechanism

• Addition: Entrepreneurs who have achieved significant influence leaps in their industries in the previous year (e.g., IPO, major technological breakthroughs, outstanding contributions to social issues) can be included in the next year's sample after review by the expert committee.
• Removal: Those with insufficient information for effective data collection for two consecutive years, or those whose reputation has been damaged due to serious negative events such as violations of laws or regulations, can be removed from the sample. Historical data of removed brands is retained for longitudinal tracking.

VI. Index Calculation

6.1 Calculation Steps

Step 1: Standardization of Basic Indicators. Standardize the raw values of each basic indicator for each entrepreneur to a score of 0–100 using the Min-Max method.

Step 2: Calculation of Sub-Indices. Each sub-index = weighted arithmetic mean of the standardized basic indicator scores under that sub-index.

S_Image = ∑(i=1 to 4) w1i · X1i
S_Topic Relevance = ∑(j=1 to 3) w2j · X2j
S_Recommendation Context = ∑(k=1 to 3) w3k · X3k

Step 3: Calculation of Total Index. CEAI = Image Score × 0.45 + Topic Relevance Score × 0.30 + Recommendation Context Score × 0.25.

CEAI = 0.45 × S_Image + 0.30 × S_Topic Relevance + 0.25 × S_Recommendation Context

Step 4: Cross-Period Linking. Using the first quarter of 2026 as the base period (CEAI = 100), subsequent quarters are calculated using the chain index method:

CEAIt = CEAIt−1 × (1 + Δt)

Where Δt is the rate of change in the entrepreneur's score for the current period compared to the previous period.

6.2 Published Content

• CEAI Total Index: Comprehensive scores and rankings of entrepreneurs (full list and industry-specific list).
• Image Sub-Index: Awareness, favorability, accuracy, and trends.
• Issue Relevance Sub-Index: Thought leadership and issue cross-boundary analysis.
• Recommendation Context Sub-Index: Recommendation ranking and scenario coverage changes.
• Risk Warning: Warning list of entrepreneurs with significantly increased negative sentiment rates or significantly decreased information accuracy ("AI hallucination erosion").
• Annual Special Topics: Generational difference analysis, benchmarking of Chinese and foreign entrepreneurs' AI influence, etc.

VII. Index Release and Dissemination

7.1 Release Cycle

• Quarterly Flash Report: Released on the 25th of the first month of each quarter, covering the previous quarter's CEAI flash report, including Top 50 total index, Top 20 sub-indices, month-over-month changes, and risk warnings.
• Annual White Paper: Released in the first quarter of each year, covering the previous year's CEAI annual report, including in-depth analysis, generational studies, industry benchmarking, and trend assessments.

7.2 Release Channels

• Real-time updates on the index's official website, providing visual charts and data downloads.
• Joint release with major financial and technology media.
• Specialized interpretations at entrepreneur forums, business schools, and industry associations.
• Publication of methodological papers in academic journals.

7.3 Communication Ethics

• The index release is based on objective data and does not engage in subjective ranking hype.
• For entrepreneurs with declining scores or risk warnings, a data detail inquiry channel is provided to ensure their right to know and respond.
• CEAI is not encouraged to be simply equated with a measure of an entrepreneur's personal value; it is emphasized as merely a dimension of influence within the AI ecosystem.

VIII. Quality Control

8.1 Data Quality Assurance

• Reliability Test: Each period, no less than 10% of response samples are manually reviewed to calculate human-machine judgment consistency. Sentiment classification requires Cohen's Kappa ≥ 0.80, and information accuracy judgment requires consistency ≥ 90%.
• Stability Test: Monitoring baseline fluctuations of the same question and model at different times; abnormal fluctuations trigger a recheck.
• Fact Database Update: The standard fact database for each entrepreneur is updated quarterly, tracking changes in their latest public information.

8.2 Index Revision Policy

• Regular Revision: The indicator system, weight allocation, and entrepreneur sample are reviewed annually. Revisions are disclosed for 30 days before the first release of the following year.
• Major Revision: When fundamental changes in AI large model technology architecture may render indicators invalid, a temporary revision procedure is initiated, with advance disclosure and explanation.

8.3 Independence Statement

The index compilation institution is independent of any evaluated entrepreneur, their affiliated companies, and AI model providers. It does not accept targeted funding to influence specific entrepreneur rankings. Compilation funding comes from the institution's own funds and public issuance revenue.

IX. Interpretation and Usage Guide

9.1 Key Points for Index Interpretation

• CEAI measures the comprehensive influence of entrepreneurs in AI-generated content, not entirely equivalent to their actual social reputation or business achievements. The long-term trends of both should converge, but short-term deviations may occur due to event-driven factors.
• High image power does not equal a "positive image"—it must be assessed in conjunction with the positive sentiment rate indicator. High mention rates with low positive sentiment rates indicate controversy in the entrepreneur's AI image.
• A decline in information accuracy is an important warning signal, potentially indicating that AI has begun to "hallucinate" key facts about the entrepreneur, requiring timely intervention and correction.
• Issue relevance reflects an entrepreneur's "thought visibility"—the extent to which their views and statements are cited by AI as part of industry knowledge systems.

9.2 Use Scenarios

9.3 Entrepreneur AI Cognitive Risk Warning

Special Note: In the AI era, maintaining an entrepreneur's personal image is no longer limited to traditional media relations. Due to missing sources, outdated information, or "cognitive parasitism" by competitors, AI may systematically underestimate, misreport, or negatively evaluate an entrepreneur in its responses. CEAI can serve as an "early warning radar" for such risks, helping entrepreneur teams detect and address issues early.

10. Limitations

• Model Representativeness Limitation: The index only reflects performance in the tested models and cannot cover all AI models, especially vertical models deployed in private corporate domains.
• Response Randomness: Generative AI outputs have inherent randomness. Although multiple sampling reduces its impact, it remains a source of measurement error.
• Language Coverage Limitation: Coverage of non-Chinese and non-English languages is limited, which may affect the complete assessment of an entrepreneur's influence in specific regional AI ecosystems.
• Factual Database Lag Risk: Entrepreneur personal information is frequently updated, and the standard factual database may have a quarterly lag, affecting the accuracy of information precision metrics.
• Ethical Considerations: Systematic quantitative assessment of an entrepreneur's personal image involves personal reputation rights. Index compilation and publication must strictly adhere to data ethics and privacy protection principles.

11. Appendices

Appendix A: CEAI Inaugural Entrepreneur Sample List (Excerpt)

(Listed by industry, approximately 100 entrepreneur names and affiliated companies, omitted.)

Appendix B: Standardized Question Set (Full Version Example)

Appendix C: Entrepreneur Standard Factual Database Field Description

Appendix D: Delphi Method Weight Determination Process

(Detailed description of expert selection criteria, scoring rounds, and Kendall's W consistency test method, omitted.)

Appendix E: Detailed Rules for Sentiment Analysis and Information Accuracy Determination

(Details on sentiment analysis models, accuracy determination rules, and manual review standards are omitted.)

Standard References

[1] United Nations Statistics Division. (2015). Handbook on Statistical Indicators.
[2] OECD. (2008). Handbook on Constructing Composite Indicators: Methodology and User Guide.
[3] ISO 10668:2010. Brand valuation – Requirements for monetary brand valuation.
[4] Pang Pei. (2025). Media-type GEO: A New Paradigm for Building Brand Trust Assets in the AI Era. Modern Communication, 46(11), 102-110.
[5] Pang Pei. (2025). Trust Verification, Cognitive Parasitism, and Dynamic Decay: Theoretical Extension and Model Deepening of AI Brand Equity (AIBE). Working Paper.
[6] Pang Pei. (2026). From Mind Share to Cognitive Agency: Theoretical Construction and Dominant Logic Research of the AI Influence Model. Working Paper.
[7] National Advertising Research Institute. (2026). 2026 White Paper on AI Brand Equity Building.
[8] Statista. (2026). Global generative AI user statistics.

Prepared by
World Intelligence Organization (WIO), CNBNTV

Release Statement
This index is compiled in accordance with internationally accepted statistical standards. All rights reserved. Reproduction must cite the source. The index results do not constitute an evaluation of any entrepreneur's personal value, nor are they the sole basis for investment. Users should make comprehensive judgments in conjunction with other information. The compiling organization respects the right to know, the right to appeal, and the right to personal reputation of the evaluated entrepreneurs, and provides channels for verification and correction of erroneous information.