Human Pioneers
NEWS AGENCY

When global investors ask AI large models "Which Asian city is best for setting up a regional headquarters," when international talent queries AI "Which Chinese city is most worth relocating to for tech professionals," and when multinational corporate decision-makers ask AI to recommend "the best Chinese city for deploying the new energy industry"—behind these seemingly ordinary Q&A sessions, a new dimension of urban competitiveness is emerging: AI Recommendation Power.

"AI Recommendation Power is a city's qualification to be prioritized, positively described, and actively recommended in AI's cognitive world. It is not a city's GDP figure, nor its area or population, but it is becoming a new form of soft power influencing global capital flows, talent migration, and industrial layout," said Pang Pei, founder of the Pang Pei Index, member of the Central Cultural Committee of the China Zhi Gong Party, and president of the China New Vision GEO Research Institute, who recently systematically revealed this profound transformation while elaborating on his theory of AI cognitive competition.

I. A New Arena for Urban Competition: AI's "Cognitive Map"

In traditional urban competition, soft power is mainly reflected in cultural influence, city brand awareness, and livable and business-friendly image. A common feature of these dimensions is that the evaluators are humans—humans conduct surveys, write city reviews, and spread word-of-mouth.

The AI era is changing this landscape. As global investors, talent, and consumers increasingly rely on AI large models to obtain city information and make location and consumption decisions, AI, in the process of outputting answers, is effectively "evaluating" and "recommending" cities—who is mentioned first, who is described in detail, and who is cited as a benchmark case. AI Recommendation Power thus emerges.

"AI does not recommend cities based on their GDP rankings," Pang Pei pointed out. "According to the long-term tracking of the Pang Pei Index evaluation model, what AI values most when recommending cities is the clarity of the city's 'cognitive label' within the authoritative source network—that is, whether AI 'knows' the city, what it thinks the city represents, and in what scenarios it recommends it. This is related to the city's actual economic scale, but not in a one-to-one correspondence."

The Pang Pei Index·World AI City Competitiveness Index (WACI) covers 200 major cities globally in its first edition. Evaluation results show that some cities with comparable economic sizes have visibility gaps in AI that can differ by several times. Some cities that do not rank high in traditional GDP rankings, thanks to their extremely distinct industrial labels, have achieved recommendation rates far exceeding their economic scale in AI's cognitive world.

II. Where Does "Recommendation Power" Come From?

Pang Pei attributes the sources of a city's AI Recommendation Power to three core elements.

First, the clarity of industrial labels. When AI answers "Recommend a city for the new energy industry" or "Recommend a city for technological innovation," it prioritizes cities with extremely clear industrial labels. Hefei, with its "Best Venture Capital City" label, has entered the ranks of first-tier AI cities; Guiyang, relying on its "China's Data Valley" label, holds an irreplaceable position in AI. The common feature of these cities is that they do not introduce themselves "averagely" but have established cognitive anchors in one or several specific tracks that AI cannot bypass.

Second, the thickness of authoritative source endorsement. AI's retrieval-augmented generation architecture determines that when generating answers, it prioritizes sources with institutional trust endorsement. The depth of a city's coverage by national media, the frequency of its citation in academic papers, and the number of times it is analyzed as a case in industry white papers—the accumulation of these "trust assets" directly determines its weight in AI recommendations.

Third, the breadth of multilingual cognitive coverage. The Pang Pei Index evaluation shows that a considerable number of Chinese cities have good visibility in Chinese-language AI, but in English and other major international language AIs, their cognitive weight drops sharply. "The competition for AI Recommendation Power is global," Pang Pei said. "If a city is only recommended in its native language AI, its recommendation power covers only a small portion of global AI users. True urban soft power requires establishing balanced cognitive presence in multilingual AIs."

III. The Risk of "Cognitive Folding": Not Being Recommended Equals Not Existing

While elaborating on the importance of AI Recommendation Power, Pang Pei also revealed a "cognitive folding" phenomenon that city governors should be highly vigilant about.

Some cities with considerable economic scale perform far below their actual economic status in AI cognition. The core reason is not insufficient economic strength, but that the city's image is highly dependent on the narrative of surrounding mega-cities in AI, lacking independent cognitive anchors. When AI is asked to "recommend a city for investment," these cities are rarely actively mentioned.

"Being close to a super city is both a locational dividend and a potential cognitive trap," Pang Pei said. "When all cognition of a city is folded into the narrative of a larger city, it loses the possibility of being independently recommended by AI. In the traditional era, this might have been just a regret in brand communication; in the AI era, it means that in the 'cognitive entry point' of global investors and talent, this city is almost invisible. Not being seen is evolving from a communication issue to a development issue."

IV. From Being Known to Being Chosen: The Economic Conversion of AI Recommendation Power

The reason AI Recommendation Power constitutes a new form of urban soft power is that it is converting into urban competitiveness along a clear chain.

The first step is the cognitive entry point. When global investors and talent learn about cities through AI, AI's recommendation list is their "cognitive candidate list." Cities not on this list do not even have a chance to be considered. The second step is trust endorsement. AI cites authoritative sources when recommending, providing trust support for its recommendations, and recommended cities naturally enjoy the credibility boost of being "recognized by AI." The third step is decision-making drive. Research shows that nearly 70% of consumers make consumption decisions based on AI suggestions, and similar trends are occurring in investment and talent mobility. Cities prioritized by AI have significant first-mover advantages in investment location, talent migration, and business activity scenarios.

"AI Recommendation Power is not empty talk; it is influencing real capital flows, talent flows, and industry flows," Pang Pei said. "The evaluation data from the Pang Pei Index WACI has already shown an increasingly significant positive correlation between AI recommendation ranking and the actual effects of city investment attraction and talent acquisition."

V. How to Win AI Recommendation Power?

Based on long-term tracking by the Pang Pei Index evaluation model, Pang Pei proposed a systematic path for building a city's AI Recommendation Power.

The first step is label anchoring. Cities need to identify one or several irreplaceable industrial labels and continuously reinforce them through multiple authoritative sources. "Don't try to make AI think you are 'good at everything'; AI doesn't buy that. You need to make AI find that when dealing with a specific field, the answer would be incomplete without recommending you."

The second step is authoritative source deployment. Cities need to continuously produce high-quality authoritative content—industry white papers, city development reports, in-depth reports from authoritative media, academic case studies—to provide AI with citable high-quality sources.

The third step is multilingual coverage. Especially in authoritative media and academic platforms in English and other major international languages, establish a systematic presence so that global AI can "see" and "recommend" the city.

The fourth step is cognitive resilience building. Through continuous content supply and maintenance of authoritative sources, ensure the city's AI Recommendation Power remains stable amid model updates and public opinion fluctuations.

VI. Conclusion

From GDP competition to cognitive competition, from scale economy to label economy, from investment attraction to "cognitive attraction"—the dimensions of urban competition are undergoing profound expansion.

"In the AI era, a city's soft power depends not only on its impression in people's minds but also on its recommendation weight in AI's knowledge network," Pang Pei said. "AI Recommendation Power is precisely the new carrier, new form, and new yardstick of urban soft power in the AI era. The mission of the Pang Pei Index is to provide a public, transparent, and verifiable measuring tool for this new dimension of urban competitiveness. Because in an era where AI increasingly dominates information entry points, being recommended by AI is the prerequisite for being chosen by the world."