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World Entrepreneur AI Influence Index

World Entrepreneur AI Influence Index

WEAI:2026

World Entrepreneur AI Influence Index

World Entrepreneur AI Influence Index

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

First Release Date: June 2026

Release Cycle: Quarterly data collection, annual comprehensive release
Language Versions: Chinese / English / French / Spanish

I. Introduction

1.1 Compilation Background

Generative artificial intelligence is reshaping the global information distribution landscape and influence generation mechanisms. According to Statista data, as of mid-2026, the global monthly active users of generative AI have exceeded 2 billion, covering more than 200 countries and regions. When users ask mainstream AI large models such as ChatGPT, Gemini, Ernie Bot, and DeepSeek, "Who is the most visionary tech entrepreneur?" or "Who is the most noteworthy business leader globally?", the answers provided by AI are becoming a key force in shaping public perception.

In this context, the image, voice, and recommendation ranking of entrepreneurs within AI large models are not merely matters of personal reputation but directly impact the brand value of their leading enterprises, confidence in capital markets, and persuasiveness in cross-cultural communication. However, there is currently a lack of a systematic, objective, and comparable evaluation tool globally to measure this new type of influence of entrepreneurs within the AI ecosystem.

1.2 Compilation Purpose

The World Entrepreneur AI Influence Index (WEAI) aims to:

• Quantitatively assess the comprehensive influence level of representative entrepreneurs from major global economies in the generated content of mainstream AI large models;
• Cross-culturally compare the differences and trends in the discourse power of entrepreneurs from different countries, industries, and generations within the AI knowledge system;
• Dynamically track the temporal changes in entrepreneurs' personal AI influence, identifying reputation risks and cognitive evolution;
• Provide a public good by offering an open, transparent, and reproducible set of benchmark data on global entrepreneurs' AI influence for academic research, business decision-making, and policy formulation.

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"
• ISO/IEC 22989:2022, "Artificial Intelligence – Concepts and Terminology"
• Pang Pei (2025), "Media-type GEO" theory
• Pang Pei (206), "AI Brand Equity (AIBE)" theory and "Six-Dimensional Model of AI Influence"
• Pang Pei (2026), "AI Cognitive Authority" theory

1.4 Relationship with CEAI

WEAI is the global expansion of CEAI (Chinese Entrepreneur AI Influence Index). Both share the core methodology and indicator system architecture, but WEAI has been expanded in the following aspects: the sample coverage has been extended from China to major global economies, language coverage has been expanded from Chinese-English bilingual to multilingual, and a cultural neutrality calibration mechanism has been added for cross-cultural comparisons.

II. Definition and Scope

2.1 Index Definition

The World Entrepreneur AI Influence Index (WEAI) is a composite statistical index that comprehensively measures the image performance, topic relevance, recommendation scenario advantages, and cross-cultural visibility of representative entrepreneurs from major global economies in the generated content of mainstream generative AI large models.

2.2 Core Construct: Entrepreneur AI Influence

Entrepreneur AI influence encompasses four core dimensions:

• Global Visibility: The breadth and frequency of entrepreneurs being mentioned in multilingual, multi-model AI-generated content.
• Cognitive Quality: The sentiment tendency, information accuracy, and depth of presentation in AI's descriptions of entrepreneurs.
• Thought Leadership: The extent to which entrepreneurs' viewpoints, statements, and methodologies are cited by AI as industry knowledge or intellectual resources.
• Recommendation Advantage: The priority ranking and scenario coverage of entrepreneurs being recommended in recommendation-type questions, such as role models or business decision-making references.

2.3 Coverage Scope

• Entrepreneur Sample: The first phase covers no fewer than 500 entrepreneurs from at least 20 major economies worldwide, spanning seven industry sectors: technology, manufacturing, finance, energy, consumer goods, pharmaceuticals, and media, taking into account different generations and enterprise types. Sample selection criteria are detailed in Chapter 5.
• Model Scope: Covers no fewer than 15 mainstream generative AI models globally:
  • Global General: OpenAI GPT-5, Google Gemini 2.0, Anthropic Claude 4, Meta Llama 4
  • China: Baidu ERNIE 4.0, ByteDance Doubao, Alibaba Tongyi Qianwen 2.5, DeepSeek V3/R1
  • Europe: Mistral Large (France), Aleph Alpha (Germany)
  • Middle East: Falcon 3 (UAE)
  • East Asia: Naver HyperCLOVA X (South Korea), Rakuten AI (Japan)
• Language Scope: The first batch covers seven languages: Chinese, English, French, Spanish, Arabic, Japanese, and Korean. Subsequent years will expand to include Russian, German, Portuguese, and Hindi.
• Time Span: Using 2026 as the base year, published annually.

III. Index Architecture and Indicator System

3.1 Index Hierarchy

The WEAI adopts a four-level hierarchical structure:

• Overall Index (WEAI): Reflects the comprehensive level of entrepreneurs' AI influence.
• Sub-Indices (4): Global Visibility Index, Cognitive Quality Index, Thought Leadership Index, Recommendation Advantage Index.
• Sub-Dimensions (12): Each sub-index comprises 3 sub-dimensions.
• Basic Indicators (24): Constitute the smallest units of data collection.

3.2 Indicator Framework

Table 1 – WEAI Indicator System

Level	Weight	Sub-Dimension	Basic Indicator
I. Global Visibility	25%	1.1 Mention Breadth	① Cross-model Mention Rate ② Cross-language Mention Rate
		1.2 Scenario Coverage	③ Industry Issue Mention Rate ④ Cross-domain Issue Mention Rate
		1.3 Temporal Stability	⑤ Quarterly Mention Volatility Coefficient ⑥ Cross-model Visibility Balance
II. Cognitive Quality	25%	2.1 Sentiment Tendency	⑦ Positive Sentiment Rate ⑧ Cross-model Sentiment Consistency
		2.2 Information Accuracy	⑨ Core Fact Accuracy Rate ⑩ AI Hallucination Frequency
		2.3 Presentation Depth	⑪ Information Density Index ⑫ Multi-dimensional Image Richness
III. Thought Leadership	25%	3.1 Opinion Citation	⑬ Direct Quotation Rate ⑭ Opinion Attribution Rate
		3.2 Issue Relevance	⑮ Industry Issue Binding Strength ⑯ Frontier Issue Relevance
		3.3 Knowledge Contribution	⑰ Methodology Citation Rate ⑱ Standard/Framework Mention Rate
IV. Recommendation Advantage	25%	4.1 Recommendation Rank	⑲ First Mention Top Placement Rate ⑳ Recommendation List Concentration
		4.2 Scenario Advantage	㉑ Industry Recommendation Scenario Coverage ㉒ Cross-industry Breakout Recommendation Rate
		4.3 Generational Advantage	㉓ Generational Label Association Strength ㉔ Priority Rank in Generational Comparisons

Weight Determination: The four sub-indices adopt an equal-weight design (25% each), reflecting the theoretical judgment that AI influence is equally important across four dimensions. Sub-dimension and basic indicator weights are determined through the Delphi method, inviting no fewer than 25 global experts in AI technology, brand management, and cross-cultural communication to participate in scoring. Weights are reviewed every two years.

3.3 Score Standardization

The raw values of each basic indicator are mapped to a 0–100 range using Min-Max Standardization:

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

Where X_min and X_max are the minimum and maximum values of that indicator among all evaluated entrepreneurs in the current period.

When comparing across years, 2026 is used as the base period (WEAI=100), and subsequent years are linked using the chain index method.

IV. Data Collection and Processing Methods

4.1 Question Set Design

To comprehensively assess the AI influence of entrepreneurs, a multi-dimensional standardized question set is designed:

(I) Visibility Question Set (Approximately 60 Questions)

Subcategory	English Example	Chinese Example
Direct Recognition	“Who is [Name]?”	“[Name]是谁？”
Achievement Evaluation	“What are [Name]'s major achievements?”	“[Name]的主要成就是什么？”
Industry Status	“Who are the most influential figures in [Industry]?”	“[Industry]领域最具影响力的人物有哪些？”

(II) Cognitive Quality Question Set (Approximately 40 Questions)

Subcategory	English Example	Chinese Example
Style Description	“What is [Name]'s leadership style?”	“[Name]的领导风格是怎样的？”
Innovation Evaluation	“Is [Name] considered innovative?”	“[Name]是否被认为具有创新精神？”
Controversy Perception	“What controversies has [Name] been involved in?”	“[Name]涉及过哪些争议事件？”

(III) Thought Leadership Question Set (Approximately 40 Questions)

Subcategory	English Example	Chinese Example
Opinion Citation	“What has [Name] said about [Industry Trend]?”	“[Name]对[Industry Trend]有何看法？”
Thought System	“What is [Name]'s business philosophy?”	“[Name]的商业哲学是什么？”
Methodology Association	“What management approach is [Name] known for?”	“[Name]以什么管理方法著称？”

(IV) Recommendation Advantage Question Set (Approximately 40 Questions)

Subcategory	English Example	Chinese Example
Leader Recommendation	“Who are the most visionary business leaders?”	“最具远见的商业领袖有哪些？”
Generational Recommendation	“Who are the most successful young entrepreneurs?”	“最成功的年轻企业家有哪些？”
Regional Recommendation	“Who are the top entrepreneurs in [Region]?”	“[Region]的顶级企业家有哪些？”

The question set for each entrepreneur can be personalized based on their industry and region. The total question set includes no fewer than 180 standard questions, translated into multiple languages simultaneously. No more than 20% of the questions are updated annually based on AI technology evolution and hot topics.

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 twice a year (mid-year and year-end) to reduce response fluctuations caused by model updates and current events.
• Repetition: Each question is independently queried 3 times on each model (with an interval of no less than 24 hours), and the average of the 3 results is taken.
• Data Volume: Single-round collection volume = 15 models × 180 questions × 500 entrepreneurs × 7 languages × 3 repetitions ≈ 28,350,000 response records (actual collection uses stratified sampling, with approximately 40 core questions per entrepreneur, totaling about 6.3 million records per round).
• Ethical Compliance: All collection targets only publicly available information and does not involve personal privacy data. Evaluated entrepreneurs have the right to be informed, to query, and to appeal and correct their own data.

4.3 Data Processing

• Named Entity Recognition (NER): A multilingual pre-trained NER model (fine-tuned based on the XLM-RoBERTa architecture) is used to extract entrepreneur names, positions, and company names from response texts. Dedicated fine-tuned models are used for each language, with a manual sampling rate of no less than 10%.
• Sentiment Analysis: For sentence-level texts describing entrepreneurs, a multilingual three-category sentiment model (positive/neutral/negative) is used. Dedicated models are used for Chinese, English, French, Spanish, and Arabic to ensure cultural context adaptation.
• Information Accuracy Determination: A standard fact database is established for each entrepreneur (full name, birth year, nationality, current position, major achievements, representative statements/works). Factual descriptions in AI responses are compared against the standard database to determine accuracy, errors, or omissions. The fact database is updated quarterly.
• Thought Leadership Identification: Through quotation markers (text within quotation marks) and attribution sentence patterns (e.g., "[Name] believes," "according to [Name]," "[Name] proposes," "[Name] argues"), it identifies whether the AI directly cites the entrepreneur's views, statements, or methodologies.
• Cross-Lingual Consistency Calibration: Cross-lingual semantic alignment analysis is performed on descriptions of the same entrepreneur across different language AI models to eliminate score differences caused by language bias.

4.4 Cultural Neutrality Calibration

Given that AI evaluations of entrepreneurs may exhibit systematic biases across different cultural contexts, WEAI introduces a cultural neutrality calibration mechanism:

• Regional Model Balance: Ensure that AI models developed in different regions have equal weight in data collection, preventing a single regional model from dominating the results.
• Language Weighted Balance: Each language is given equal weight in the composite score to prevent English corpus dominance from systematically lowering scores for entrepreneurs from non-English regions.
• Cultural Context Adaptation: In sentiment analysis and semantic judgment, calibration is performed to adapt to differences in positive and negative expressions across cultures (e.g., implicit expressions in East Asian cultures are not misjudged as neutral or negative).

4.5 Outlier Handling

• When a single collection result deviates significantly (more than 3 standard deviations from the mean), an additional supplementary query is conducted and replaces the outlier.
• If a particular AI model experiences a systemic failure or is unavailable in a region, the weight of that model for that round is temporarily distributed to other similar models, with a public explanation in the report.

V. Entrepreneur Sample Selection

5.1 Sample Selection Principles

• Global Representation: Cover major global economies (initially selecting 20 countries/regions based on GDP and technological influence), ensuring representation from all continents.
• Industry Diversity: Encompass seven industry sectors: technology, manufacturing, finance, energy, consumer goods, pharmaceuticals, and media.
• Generational Balance: Include entrepreneurs from different birth decades (1940s to 1990s) to reflect generational differences in AI influence among entrepreneur groups.
• Diverse Enterprise Types: Include founders/leaders from diverse enterprise types such as listed companies, unicorns, family businesses, and social enterprises.
• Data Measurability: Entrepreneurs must have sufficient information volume (richness of public reports) in major global AI models to support data collection needs for the standardized question set.

5.2 Initial Coverage Regions and Sample Allocation

Region	Country/Region	Estimated Sample Size	Example Representative Entrepreneurs
East Asia	China, Japan, South Korea	120	Ren Zhengfei, Lei Jun, Masayoshi Son, Lee Jae-yong
North America	United States, Canada	120	Elon Musk, Satya Nadella, Jensen Huang
Europe	UK, France, Germany, Switzerland, Netherlands	100	Bernard Arnault, Larry Fink
Southeast Asia/South Asia	India, Singapore, Indonesia	50	Mukesh Ambani, Grab Founder
Middle East/Africa	UAE, Saudi Arabia, South Africa, Nigeria	40	Aliko Dangote
Latin America	Brazil, Mexico	30	Jorge Paulo Lemann
Oceania	Australia	20	Atlassian Founders
Other	—	20	Supplementary slots

See Appendix A for the full list. The list is nominated by a global expert committee and determined through multiple rounds of selection, with reviews and adjustments every two years.

5.3 Dynamic Adjustment Mechanism

• New Inclusions: Entrepreneurs who have achieved a significant leap in global influence during the previous evaluation cycle may be included in the next year's sample after review by the expert committee.
• Exit Mechanism: Those whose information volume is insufficient for effective data collection for two consecutive years, or whose role has fundamentally changed due to retirement, death, or major negative events, may be removed from the sample. Historical data of exiting entrepreneurs is retained for longitudinal tracking.

VI. Index Calculation

6.1 Calculation Steps

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

Step 2: Calculation of Sub-dimension Scores. Score for each sub-dimension = arithmetic mean of the standardized scores of the basic indicators under that sub-dimension.

Step 3: Calculation of Sub-indices. Each sub-index = weighted arithmetic mean of the sub-dimension scores under that sub-index.

Sk=∑j=13wkj⋅Dkj Sk​=∑j=13​wkj​⋅Dkj​

Where SkSk​ is the kk-th sub-index, wkjwkj​ is the weight of the sub-dimension, and DkjDkj​ is the sub-dimension score.

Step 4: Calculation of the Total Index. WEAI = weighted arithmetic mean of the four sub-indices (equal weights).

WEAI=0.25×S Visibility + 0.25×S Cognitive Quality + 0.25×S Thought Leadership + 0.25×S Recommendation Advantage WEAI=0.25×S Visibility​+0.25×S Cognitive Quality​+0.25×S Thought Leadership​+0.25×S Recommendation Advantage​

Step 5: Cross-Year Linkage. Using 2026 as the base year (WEAI=100), subsequent years are calculated using the chain index method.

6.2 Published Content

• WEAI Global Ranking: Comprehensive scores and rankings of 500 entrepreneurs worldwide.
• Regional Rankings: Rankings for seven major regions: North America, Europe, East Asia, Southeast Asia, Middle East/Africa, Latin America, and Oceania.
• Industry Rankings: Rankings for seven major industries: Technology, Manufacturing, Finance, Energy, Consumer Goods, Pharmaceuticals, and Media.
• Special Lists: Top 100 in Thought Leadership, Top 100 in Global Visibility, Top 100 in Recommendation Advantage.
• Generational Lists: Top 50 Young Entrepreneurs' AI Influence (under 45), Lifetime Achievement List (over 70).
• Annual In-Depth Report: Trend analysis, cross-cultural comparisons, and case studies.

VII. Index Release and Dissemination

7.1 Release Cycle

• Annual Comprehensive Release: The WEAI Global Ranking and all sub-rankings for the previous year are released in the first quarter of each year.
• Quarterly Briefs: Quarterly dynamic tracking briefs on the AI influence of key entrepreneurs (limited to those who consent to public data).

7.2 Release Channels

• Real-time updates on the official index website, with visual charts and data downloads (open access).
• Joint release by major global financial and technology media (in English, Chinese, French, and Spanish).
• Special releases at global platforms such as the World Economic Forum and Davos Annual Meeting.
• Publication of methodological papers in academic journals.
• Joint release of regional rankings by regional partners.

7.3 Communication Ethics

• The index release is based on objective data and avoids subjective ranking hype.
• For entrepreneurs whose scores have declined, a data detail inquiry channel is provided to ensure the right to information and response.
• Clear statement: The WEAI measures entrepreneurs' influence in AI-generated content and does not equate to a comprehensive judgment of their personal achievements or moral character.

VIII. Quality Control

8.1 Data Quality Assurance

• Reliability Testing: At least 10% of response samples from each collection round are manually reviewed. Sentiment classification requires Cohen's Kappa ≥ 0.80, and information accuracy judgment requires human-machine consistency ≥ 90%.
• Stability Check: Continuous monitoring of baseline fluctuations for the same question and model over different time periods; abnormal fluctuations trigger a re-check.
• Fact Base Update: The standard fact base for each entrepreneur is updated quarterly to track the latest changes in publicly available information.
• Multilingual Cross-Validation: Continuous monitoring of core fact consistency for the same entrepreneur across AI models in different languages; systematic deviations are calibrated promptly.

8.2 Index Revision Policy

• Regular Revision: The indicator system, weight allocation, and entrepreneur sample are reviewed every two years. Revisions are announced for public comment at least 60 days before the release of the first issue 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 notice and full 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 the ranking of specific entrepreneurs. Compilation funding comes from the institution's own funds, public issuance revenue, and non-targeted research grants. The institution publishes an annual independence statement and funding source report.

9. Interpretation and Usage Guide

9.1 Key Points for Index Interpretation

• WEAI measures the comprehensive influence of entrepreneurs in AI-generated content, not equivalent to their actual wealth, social reputation, or moral character. Long-term trends of the two should converge, but short-term deviations may occur due to event-driven factors.
• High visibility does not equal high reputation—requires comprehensive judgment combined with cognitive quality scores. High mention rates with low positive sentiment rates may indicate significant controversy in the entrepreneur's AI image.
• Thought leadership reflects the entrepreneur's "knowledge contribution"—whether their views, statements, and methodologies are cited by AI as an integral part of the industry knowledge system.
• When conducting cross-cultural comparisons, note that the natural visibility of entrepreneurs from different regions in AI models may have systematic differences due to factors such as corpus distribution and model training data composition. WEAI uses language-weighted balancing to minimize such biases as much as possible.

9.2 Usage Scenarios

User	Usage Scenario
Entrepreneurs and their teams	Understand their influence positioning in the global AI ecosystem, identify cognitive advantages and risks
Corporate brand and PR departments	Integrate founder AI influence into corporate reputation management systems
Investment and financial institutions	Use entrepreneur AI influence as a reference dimension for evaluating corporate intangible assets
Academic and research institutions	Study the construction mechanisms and cross-cultural differences of entrepreneur influence in the AI era
Government and trade promotion agencies	Assess the global AI discourse power of the domestic entrepreneur community, assist in formulating economic diplomacy strategies
Media and content platforms	Understand the visibility landscape of global entrepreneurs in the AI content ecosystem

10. Limitations

• Model Representation Limitations: The index only reflects performance in the tested models and cannot cover all AI models. In particular, vertical models deployed privately in various countries are not included.
• Response Randomness: Generative AI outputs have inherent randomness. Although multiple sampling and two rounds of annual collection reduce the impact, it remains a source of measurement error.
• Language Coverage Limitations: Entrepreneurs in languages not among the first covered may have systematically lower scores on some indicators due to insufficient corpus. This will gradually improve as language coverage expands.
• Fact Base Lag Risk: Entrepreneur personal information is updated frequently, and the standard fact base may have a lag within a quarter, affecting the accuracy of information accuracy indicators.
• Residual Cultural Bias: Although cultural neutrality calibration has been introduced, cultural biases in AI model training data may still affect evaluation results in ways that are difficult to completely eliminate.
• Ethical Prudence: 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, and the evaluated individuals have full rights to information, inquiry, and response.

11. Appendices

Appendix A: WEAI Inaugural Entrepreneur Sample List (Excerpt)

(List of approximately 500 entrepreneurs by region and industry, with affiliated companies, omitted.)

Appendix B: Standardized Question Set (Full English Version Example)

(List of no fewer than 180 standardized questions across four categories: Visibility, Cognitive Quality, Thought Leadership, and Recommendation Advantage, omitted.)

Appendix C: Entrepreneur Standard Fact Base Field Description

Field	Description	Update Frequency
Full Name	Native language name and common Latin spelling	Real-time
Nationality/Region	Primary nationality and place of residence	Annual
Year of Birth	Year only	Annual
Current Position	Primary company and job title	Quarterly
Key Achievements	No more than 5 recognized core achievements	Quarterly
Representative Statements/Works	3-5 representative viewpoints/publications that have been publicly reported	Quarterly

Appendix D: Delphi Method Weight Determination Process

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

Appendix E: Multilingual Sentiment Analysis Model Description

(Provides training data sources, performance metrics, and cultural adaptation methods for sentiment analysis models in various languages, omitted.)

Appendix F: Cross-lingual Semantic Alignment Calibration Method

(Describes technical solutions for eliminating cultural bias in cross-lingual comparisons, omitted.)

Normative 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]ISO/IEC 22989:2022. Artificial intelligence – Concepts and terminology.

[5] Pang Pei. (2025). Media-type GEO: A New Paradigm for Building Brand Trust Assets in the AI Era. 

[6] Pang Pei. (2026). Trust Verification, Cognitive Parasitism, and Dynamic Decay: Theoretical Extension and Model Deepening of AI Brand Equity (AIBE).

[7] Pang Pei. (2026). From Mind Share to Cognitive Agency: Theoretical Construction and Dominant Logic Research of the AI Influence Model.

[8] Pang Pei. (2026). Deepseek-type AI Empowering International Communication of Chinese Civilization: Opportunities, Challenges, and Pathways. China Development.

[9] Statista. (2026). Global generative AI user statistics.
[10] Gartner. (2025). Predicts 2026: AI reshapes organic search and brand discovery.

Compiling Organization
World Intelligence Organization (WIO), CNBNTV

Release Statement

This index is compiled in accordance with internationally accepted statistical norms. 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 or business decisions. The evaluated entrepreneurs have the right to be informed, to inquire, and to respond. The compiling organization publishes an annual independence statement.