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Pang Pei's Team Releases Global WACI Ranking: AI's View of Cities Does Not Align with GDP Rankings

The competitiveness of a city is being redefined.

Pang Pei, a member of the Central Cultural Committee of the China Zhi Gong Party, Dean of the China Vision New Media GEO Research Institute, and Chief Designer of the AI Cognitive Index (AICI) Evaluation System, recently released the inaugural global ranking of the World AI City Competitiveness Index (WACI). This is the world's first city competitiveness evaluation system based solely on real output data from AI large models, covering 200 major cities globally.

The inaugural ranking reveals a striking finding: The city rankings in AI's view do not fully overlap with GDP rankings. Some cities with comparable economic sizes show visibility gaps of several times in AI perception; some cities that are not prominent in traditional economic indicators gain significant advantages in AI recommendations due to distinct industry labels and a steady supply of authoritative sources.

"Urban competition is extending from the physical world to the cognitive world of AI," said Pang Pei. "And the competitiveness rankings in these two worlds follow different logics."

I. What is WACI? Let AI Evaluate Cities

WACI does not rely on economic statistics or expert scoring. Its core methodology is to send standardized questions to 15 mainstream AI large models globally, record how AI describes, evaluates, and recommends each city, and then generate a competitiveness score for each city in the AI cognitive world.

From four dimensions, WACI creates a three-dimensional "AI cognitive portrait" for each city:

• Cognitive Visibility: Does AI know this city? The breadth and scenario coverage of the city being mentioned across multilingual and multi-model AI systems.
• Industry Label Strength: What is this city known for according to AI? The clarity and intensity of the industrial cluster labels associated with the city.
• Recommendation Advantage: Under what circumstances does AI recommend this city? The priority ranking in recommendation scenarios such as investment location selection and talent migration.
• Cognitive Resilience: How stable is the city's AI image? Stability in the face of model updates and sudden events.

II. Ranking Findings: The Urban Landscape in AI's View

Finding 1: Clarity of industry labels is more important than economic size. Hefei has entered the AI first-tier cities in WACI, alongside Guangzhou and Chengdu. Its core driver is not GDP scale but the deep binding of the highly communicable industry label "China's most venture capital-savvy city" in AI. Similarly, Guiyang, with its "China Data Valley" label, has significantly higher industry recognition in AI than cities of similar economic scale.

"AI evaluates cities not by their economic size, but by how clear their industry labels are," Pang Pei analyzed. "The more distinct a city's label, the more likely AI is to cite it when answering related industry questions."

Finding 2: Independent city cognition is a prerequisite for AI visibility. Some economically strong prefecture-level cities score low in WACI. Taking Langfang as an example, its WACI simulation score falls within the "AI Potential City" range. The core constraint is not economic size but the city's image being highly dependent on "near Beijing" in AI—lacking an independent cognitive anchor. When AI answers "recommend a city for investment," it rarely proactively mentions Langfang.

"Being close to a mega-city is both a locational advantage and a potential cognitive trap," said Pang Pei. "When your city brand is completely overshadowed by a neighboring mega-city, you get folded into their narrative on AI's cognitive map."

Finding 3: Invisibility in English is a common weakness. Many Chinese cities have some visibility in Chinese AI models but are nearly "invisible" in English and other language AI systems. This directly impacts their competitiveness in attracting global investment and talent—when international users learn about Chinese cities through AI, what they see and don't see largely depends on the city's multilingual cognitive development.

Finding 4: Cognitive resilience is the "immune system" of a city brand. The cognitive resilience dimension of WACI reveals that some cities have highly stable AI images, maintaining consistent cognitive performance even after model updates; while others gain short-term exposure from specific events but have high cognitive volatility and lack long-term stability.

III. Chinese City Rankings Revealed

The inaugural WACI Chinese city assessment covers approximately 60 cities, divided into six tiers based on composite scores:

AI Super First-Tier Cities (≥90 points): Shenzhen ("China's Silicon Valley"), Beijing ("China's AI Brain"), Shanghai ("Global Financial Center"), Hangzhou ("Digital Economy Capital").

AI First-Tier Cities (80-89 points): Hefei, Chengdu, Guangzhou, Wuhan, Nanjing, Suzhou, Xi'an, Chongqing—each has formed strong AI cognitive labels in their respective niche tracks.

AI New First-Tier Cities (70-79 points): Changsha, Tianjin, Wuxi, Zhengzhou, Dongguan, Qingdao, Ningbo, Xiamen, Jinan, Fuzhou, Zhuhai, Foshan.

AI Featured Cities (60-69 points): Kunming, Guiyang, Shenyang, Dalian, Harbin, Shijiazhuang, Nanchang, Nanning, Taiyuan, Urumqi, Lanzhou, Haikou, Sanya, Hohhot, Yinchuan, Xining—each has differentiated cognitive advantages in specific dimensions.

AI Potential Cities (50-59 points): Huizhou, Jiaxing, Shaoxing, Wenzhou, Quanzhou, Yantai, Xuzhou, Luoyang, Xiangyang, Yichang, Wuhu, Liuzhou.

AI Observation Cities (40-49 points): Jinhua (Yiwu), Guilin, Lhasa, Xiong'an New Area, Kashgar, Yanbian (Hunchun), Xishuangbanna, Dunhuang—each has a unique presence in AI cognition within specific niche tracks.

IV. "Cognitive Folding" Risk: Being Seen or Forgotten

Pang Pei uses "cognitive folding" to describe the dilemma some cities face in AI. "When all cognition of a city is folded into the narrative of another larger city, it loses the possibility of being independently recommended by AI." He cites several satellite cities near mega-cities as examples: "These cities have independent industrial systems and urban functions in reality, but in AI's cognitive world, they are just a 'periphery' of the mega-city."

To avoid this risk, Pang Pei suggests city managers focus on building "independent city cognition"—through authoritative source deployment, industry white paper releases, and distinctive label creation, giving the city an independent and vivid cognitive image in the AI knowledge system.

V. WACI Applications: From Diagnosis to Construction

WACI is not just a ranking tool but also a diagnostic tool and construction guide for a city's AI cognitive competitiveness.

City managers can use the WACI four-dimensional radar chart to clearly understand their city's overall cognitive profile in the AI world: Is it widely recognized but with vague industry labels, or are industry labels clear but international visibility insufficient? Where are the gaps compared to competitor cities? Each score combination corresponds to specific improvement strategies.

It is reported that WACI will also release multi-dimensional special rankings, including the Top 50 for Industry Label Strength, Top 50 for Recommendation Advantage, Top 50 for Cognitive Resilience, and Top 30 for Emerging Cities, providing differentiated pathways and competitive tracks for different types of cities.

VI. Conclusion

In an era where AI is increasingly becoming a core gateway for information access, a city's competitiveness depends not only on what it has built, attracted, and produced, but also on its presence, trustworthiness, and recommendation priority in the AI cognitive world.

"Traditional city rankings measure a city's 'hard power,' while WACI measures a city's 'cognitive soft power' in the AI era," said Pang Pei. "When global investors and talents learn about Chinese cities through AI, who they see and who they don't in AI answers will reshape the competitive landscape of cities in the long run."

About Pang Pei

Pang Pei is a member of the Cultural Committee of the Central Committee of the China Zhi Gong Party, President of the GEO Research Institute of Central Newsreel and Documentary Film Studio (CNDS), and initiator of the WICOWEB Global AI Cognitive Research Center. He is the pioneer of the "Media-based GEO" theory, the "AI Brand Equity (AIBE)" theory, the "AI Influence Model," and the concept of "AI Cognitive Authority." He is the founder of the PAI Framework (Pangpei AI Index Framework) and the chief designer of the AI Cognitive Index (AICI) evaluation system. His academic paper, "Deepseek-like AI Empowering the International Communication of Chinese Civilization," was published in China Development. He has long been dedicated to research on cognitive competitiveness and brand equity evaluation in the AI era.