Abstract
This research report systematically examines the differentiated development paths of artificial intelligence in China and Europe, aiming to transcend the outdated paradigm of "zero-sum game." The study finds that China, characterized by "application-driven" and "scale advantages," and Europe, defined by "value orientation" and "regulation-first" approaches, exhibit significant complementarity across three dimensions: technology R&D, industrial application, and governance rules. Through in-depth analysis of three case studies—localized adaptation of autonomous driving, export of AI security detection technology, and collaborative business education innovation—this report demonstrates the feasibility of building a China-Europe AI community through institutional mutual learning and technological collaboration. The research also identifies Europe’s structural dilemma of "excessive regulation with insufficient competitiveness" and China’s shortcomings in foundational algorithms and high-end chips. On this basis, the report proposes three policy pillars: establishing a system of Mutual Recognition Agreements (MRAs), creating a joint mechanism for tackling "global AI long-tail challenges," and developing a normalized closed-loop "China-Europe AI Governance Observatory." These recommendations provide a strategic pathway for deepening pragmatic China-Europe cooperation.
Keywords
Artificial Intelligence; China-Europe Cooperation; Governance Synergy; Technological Complementarity; Policy Recommendations
1. Introduction: The Logic of Competition and Cooperation in the AI Era
Artificial intelligence is reshaping the global economic landscape and geopolitical order with unprecedented depth and speed. As two major global forces in AI, China and the European Union have pursued markedly different development paths. Leveraging its vast data ecosystem and comprehensive industrial system, China emphasizes "application-driven" and "scale advantages," viewing AI as a core engine for new productive forces. In contrast, the EU, grounded in the Artificial Intelligence Act, adheres to a "value-oriented" and "regulation-first" approach, seeking balance between technological innovation and the protection of fundamental rights.
However, against a backdrop of increasing risk of global technological decoupling and intensifying geopolitical competition, the zero-sum mindset neither serves the realization of either side's strategic objectives nor adequately addresses the common challenges posed by AI technology. In fact, the competition between China and Europe in AI is not a zero-sum game of who defeats whom, but rather an institutional proposition of how to find complementarity amidst differences and achieve coexistence through competition. As Fang Yue, Director of the AI and Management Innovation Research Center at the China Europe International Business School (CEIBS), noted: "Cooperation between China and Europe is not about who is stronger than whom. China values agility and speed, while Europe emphasizes ethics and safety. Both sides need to learn from each other and complement each other's strengths."
The core research question of this report is: Are the fundamental differences in China-EU AI development paths obstacles to cooperation, or are they the very basis for complementarity? Under conditions of increasing politicization of technology governance, can both sides construct a collaborative paradigm that transcends "great power game" dynamics through institutional mutual learning? By systematically scanning the strategic landscape on both sides, deeply analyzing typical cases, and identifying cooperation obstacles and opportunities, this report aims to provide a pragmatic and feasible path for policy decision-makers towards China-EU AI synergy.
2. Current Situation Scan: Two Divergent AI Evolution Paths
2.1 China’s “Scale and Application” Advantages
The driving force behind China’s AI development stems from three mutually reinforcing elements: data ecosystem, application scenarios, and institutional momentum.
At the data level, China’s vast digitally-savvy population and high mobile internet penetration provide a rich, low-cost data stream for training large AI models. It is estimated that China’s trainable data resources far exceed those of the EU, the latter being about one-fifth of America’s. This "data dividend" gives Chinese companies a unique advantage in model iteration speed and scenario adaptation capability.
At the application level, AI has deeply integrated into various fields such as smart cities, public safety, industrial internet, and e-commerce. China not only boasts the world’s most active AI application market but also one of the most extensive expansions of AI industrial boundaries. For example, in embodied intelligence, the localization rate of core components for humanoid robots in China has exceeded 70%, significantly ahead of the EU’s reliance on American companies for similar industries. Domestic large models like DeepSeek and Kimi achieve high performance at significantly lower costs than their US counterparts, enabling the empowerment of small and medium-sized enterprises.
At the institutional level, the Chinese government, through policy documents such as the "New Generation Artificial Intelligence Development Plan" and the "Action Plan for the Construction of New Data Centers (2023-2025)," demonstrates strong decision-making execution capability in areas including data center construction, long-term R&D planning, and cross-sectoral resource integration. Leveraging the "West-East Electricity Transmission" project and abundant green energy resources (solar, wind), China essentially faces no energy supply bottleneck restricting AI development. Central and local finance, through special funds and industrial parks, form efficient coordination with corporate innovation activities.
2.2 Europe’s “Regulatory and Ethical” Leadership
In stark contrast to China, the EU has carved out a differentiated path centered on "norm-shaping." Professor Lu Chuanying from the Center for Strategic and Security Studies at Tsinghua University summarizes the EU’s AI strategy into three pillars: norm-shaping, rule-setting, and technological sovereignty.
At the norm-shaping level, the EU Artificial Intelligence Act, which came into full effect in August 2024, is the world’s first comprehensive legal framework for regulating AI. It establishes a risk-based governance model, categorizing AI systems into four risk levels (unacceptable, high, limited, and minimal) with corresponding regulatory requirements and penalties. This "hard law" regulatory model has provided a "European paradigm" for global AI governance, generating a significant "Brussels Effect."
At the rule-setting level, the EU actively promotes global AI ethical guidelines and governance frameworks in multilateral platforms such as the UN, OECD, G7, and G20. During the G7 Hiroshima Summit in 2023, the EU facilitated the launch of the "Hiroshima AI Process." Concurrently, through initiatives like Digital Economy Partnerships with Africa and the "ASEAN-EU Strategic Partnership," the EU promotes its governance experience to the Global South.
At the technological sovereignty level, the EU has proposed investing 30 billion euros in AI infrastructure and is building a network of 13 regional AI factories spanning 17 member states and 2 participating countries, with the Munich hub being the first exascale supercomputing cluster in the EU. However, this strategy faces dual constraints of insufficient energy supply and funding challenges – each gigawatt-level data center requires approximately one gigawatt of electricity, a load for which European grid designs were not originally intended.
2.3 Comparative Analysis and Complementarity Identification
The differences between China and the EU’s AI development paths can be summarized as a contrast between "theoretical-driven innovation" and "application-driven innovation." The EU boasts formidable strength in basic research and underlying algorithms – institutions like the Max Planck Society in Germany and the French National Centre for Scientific Research have deep foundations in mathematics, physics, and computer science. However, the EU’s research strengths have not effectively translated into commercial applications and industrial competitiveness. In 2023, venture capital investment in the EU’s AI sector was only about $8 billion, far less than the $68 billion in the US and $15 billion in China.
Conversely, China excels in engineering and large-scale application but still faces bottlenecks in high-end AI chip manufacturing and lags behind Europe in fundamental algorithms and theoretical innovation. This structural complementarity – Europe’s "algorithm strength" paired with China’s "computing power and scenario strength" – precisely forms the logical starting point for cooperation. As some scholars have pointed out, "Both sides can establish open-source whitelists, set up China-EU AI patent pools, co-create national-level AI laboratories, and enable cross-border collaboration without leaking raw data."
3. Case Study Analysis: Practical Paths for China-EU AI Cooperation
3.1 Case One: The "Standard Interoperability" Challenge of Autonomous Driving
Chinese automakers face a deep-seated negotiation between technology solutions and local laws when exporting autonomous driving vehicles to the European market. The practice of ZhiJia Xincheng (formerly ZhiJia Continental) – a joint venture between Horizon Robotics and Germany’s Continental AG – vividly presents both the complexity of this challenge and a potential solution path.
The establishment of ZhiJia Xincheng itself was a four-year process of institutional mutual learning. Before the joint venture was formed, the two sides held over a thousand meetings, engaging in "comprehensive exchanges" from preliminary research departments to production units, and from frontline employees to group CEOs. After its establishment in 2022, ZhiJia Xincheng quickly integrated its autonomous driving solutions into Chinese OEMs like Chang‘an, Chery, and BYD.
However, when this solution entered the European market, "acclimatization" issues immediately surfaced. Li Biao, CEO of ZhiJia Xincheng, candidly stated: "Our original advanced autonomous driving solution was used to maneuvering forward in crowded, high-traffic situations to avoid stopping for too long – a typical ‘experienced Chinese driver’ style. However, this was considered a violation of German ‘right-of-way’ rules. According to local traffic regulations, the vehicle must wait and cannot force its way through." The core issue this case reveals is: Autonomous driving is not just a competition of computing power; it is about the AI’s cognitive adaptation to heterogeneous legal environments. The extremely detailed local regulations across European countries – from the GDPR’s strict protection of data privacy to Germany’s precise traffic right-of-way rules – constitute an institutional threshold for implementing Chinese autonomous driving solutions.
Worth noting is that the joint venture model of ZhiJia Xincheng itself provides a solution. Leveraging Continental’s European compliance network, the compliance certification time for its engineering test vehicles was reduced from the usual six months or more to less than one month. After strategic adjustments – modifying the "aggressive" style into a "defensive deceleration" compliant with European right-of-way rules – ZhiJia Xincheng’s ADAS system "Haoyue" received praise from European customers as "one of the best ADAS systems," with expected overseas revenue share surpassing domestic revenue within two years.
The policy implication of this case is: China and Europe should establish a "traffic autonomous driving data sandbox" to exchange long-tail edge case data on autonomous driving behavior under different traffic signage, without involving national security concerns. The heterogeneous integration of China’s high-density traffic environment training data with Europe’s fine-grained regulatory constraints can jointly improve AI robustness and lay the foundation for developing globally applicable safety standards for autonomous driving.
3.2 Case Two: From “Regulatory Confrontation” to “Compliance Co-governance”
With the entry into force of the EU Artificial Intelligence Act, compliance review of AI systems has become a threshold any technology product must cross to enter the European market. Traditionally viewed as a trade barrier or regulatory hurdle, the practice of Chinese AI security companies shows that regulatory compliance can be turned into an opportunity for technological cooperation.
The introduction of RealAI’s AIGC detection platform, DeepReal, into the Italian market serves as a typical case of "compliance co-governance." The EU AI Act imposes strict requirements on generative AI, including transparency, data governance, and copyright protection, creating rigid demand for AIGC detection and security certification. RealAI’s detection algorithm is competitive in efficiency and accuracy. Combined with Europe’s high-standard ethical framework, compliance procedures, and third-party neutral certification mechanisms, both sides are jointly building a globally-oriented AI security certification system.
The core insight of this case is: China and Europe can truly achieve a "handshake on technological security values." China provides efficient, low-cost detection algorithms; Europe provides a high-standard ethical framework and compliance processes. Their combination not only serves both markets but also has the potential to become a benchmark for global AI security certification. As some scholars have noted, although the EU’s "regulation-first" strategy somewhat suppresses innovation vitality, it also creates a "trust dividend" – companies that meet EU standards can obtain a "passport" to access broader international markets. For Chinese technology companies, proactively aligning with EU regulatory standards and transforming compliance costs into competitive advantages is a strategic choice for achieving sustainable globalization amidst the current geopolitical landscape.
3.3 Case Three: Synergy between Business Education and Industrial Innovation
Beyond intergovernmental cooperation and corporate transactions, educational and knowledge platforms play a unique role in bridging the "technological culture" gap between China and Europe. The practice of the AI and Management Innovation Research Center at the China Europe International Business School (CEIBS) provides a valuable reference.
In May 2026, marking the 51st anniversary of China-EU diplomatic relations, CEIBS held the China-EU Anniversary Commemorative Forum and the 11th CEIBS Innovation Conference in Beijing. On this occasion, Deputy Head of the EU Delegation to China, Mattias Lentz, stated: "The landscape of EU-China relations in the next fifty years depends on the ability to turn challenges into collaboration." Fang Yue, Director of the CEIBS AI and Management Innovation Research Center, further elaborated on the essence of this collaboration: "If you just imagine yourself as a Chinese company wanting to go global, the path won’t go very far. You need to position yourself as a global enterprise, thinking about how to leverage global resources to do your business better."
The center’s practice focuses on building a "two-way case library" – systematically comparing the rapid iteration strategies of Chinese companies under "application-driven" conditions with the lean management practices of European companies in "industrial digitalization." The value of this knowledge product lies in providing business managers with a "Chinese-Western integrated" AI management paradigm, effectively eliminating implementation obstacles caused by differences in "technological culture." For instance, Chinese companies favor "small steps, fast iteration, rapid trial and error," while European companies‘ stringent requirements for engineering reliability are sometimes perceived as "slowing down the pace." Through case-based teaching, managers from both sides can understand each other’s decision-making logic and find a balance between "agility" and "robustness."
The implication of this case extends beyond the education sector itself: The deep-seated obstacles to China-EU AI cooperation often lie not in technology per se, but in differences in organizational culture and decision-making inertia. Through "soft infrastructure" development such as business education, executive training, and joint research, both sides can cultivate a group of "bridge talent" who understand both technology and cross-cultural management, providing human capital support for pragmatic cooperation at the industrial level.
3.4 Integrated Analysis of the Cases
The three cases reveal the logic and pathways of China-EU AI cooperation from different dimensions. The autonomous driving case focuses on "how institutional differences can be transformed into complementary advantages through a joint venture model." The AIGC detection case demonstrates "how regulatory compliance can shift from an obstacle to a cooperation opportunity." The business education case highlights "the intermediary role of knowledge platforms in bridging cultural differences."
The common insight across all three cases is: Deepening China-EU AI cooperation requires not just technological alignment, but systematic mutual learning across institutions, standards, culture, and human capital. While breakthrough cooperation at a single point has value, only by building a multi-level, multi-dimensional collaboration network can the paradigm truly shift from "competition" to "coexistence."
4. Deep Analysis: Cooperation Obstacles and Strategic Opportunities
4.1 The EU’s Structural Dilemma: “Strong Regulation, Weak Competitiveness”
The fundamental challenge facing the EU’s AI strategy can be summarized as an imbalance of "excessive regulation with insufficient competitiveness." This dilemma stems from multiple structural constraints.
First is the innovation suppression effect. The complex risk management and compliance system of the AI Act, while aiming to ensure trustworthiness and safety, fails to fully account for the uncertainty and iteration speed inherent in AI technology evolution. Its "static risk classification framework" struggles to adapt to "dynamic innovation pace," leading to high compliance costs and suppressed innovation vitality, especially for SMEs. Second are structural deficiencies in the industrial ecosystem. Europe lags significantly behind the US and China in venture capital ecosystems, business model innovation, and technology commercialization – a paradox of "strong research but weak industry" reflecting deeper contradictions in the innovation system. Third is the strategic autonomy dilemma in international cooperation. The EU often finds itself in a structural contradiction between the aspiration for "strategic autonomy" and dependence on "Transatlantic security" regarding China’s AI policy, leading to increasingly stringent security reviews of Chinese tech companies like Huawei and ZTE, a trend also spreading to the AI field.
However, dilemmas also contain transformation opportunities. The "low-cost, high-efficiency" pathways provided by Chinese companies like DeepSeek present pragmatic cooperation options for the EU. Countries like Germany and France have begun to draw on DeepSeek’s development approach. Its "decentralized" and "resource adaptation" philosophy aligns well with the EU’s need to explore AI development models suited to its own endowments.
4.2 Compliance Hurdles and Geopolitical Risks for Chinese AI Going Global
For Chinese AI companies entering the European market, three realistic constraints exist.
First is data privacy regulation. Any AI product entering the European market must comply with the GDPR’s requirements – transparency and user consent in training data collection and user data processing, and ensuring European data is not arbitrarily transferred outside the region. This poses serious challenges to the data governance capabilities of Chinese companies. Second is national security review. The "pan-security" tendency in Europe’s China AI policy often places technological cooperation under the microscope of geopolitics. Third are differences in ethics and values. European policymakers worry that large models developed in China, if embedded with preferences not aligned with Western ethical standards, would challenge the ethical principles upheld by the EU.
The key to addressing these challenges lies in Chinese AI companies shifting from "going global" to "integrating globally" – as Fang Yue stated, "positioning yourself as a global enterprise, thinking about how to leverage global resources to do your business better." The practice of Zhuoyu Technology (Zoyte) establishing its European headquarters in Braunschweig, Germany, shows that deep localized R&D and ecosystem co-building are effective pathways to overcome compliance and technology adaptation hurdles.
4.3 The Dual Impact of Geopolitical Variables on Technological Cooperation
Geopolitical factors have a dual and contradictory impact on China-EU AI cooperation. On one hand, the US, through its "AI Action Plan" promoting a "full-stack AI export package" and an "America-first standards" strategy, attempts to exchange technology for allied recognition of its AI governance rules. This puts pressure on the EU regarding cooperation with China, stemming from Transatlantic alliance dynamics. On the other hand, the EU’s deep dependence on US tech giants (Google, Microsoft, etc.) makes it difficult to readily accept US technical standards. This dilemma of "dual dependence" paradoxically creates a strategic window for China-EU cooperation.
Professor Lu Chuanying from Tsinghua University offers a valuable perspective: "The ample consensus between China and Europe on principles like ‘risk classification’ and ‘human control’ is a crucial foundation for cooperation between the two sides." During the window period before global AI governance rules are finalized, China and Europe share the responsibility to resist the campification of technology governance and work together to foster a global AI governance system characterized by "technological inclusiveness, coordinated rules, and shared responsibility."
5. Policy Recommendations: Building the Three Pillars of “China-EU AI Synergy”
Based on the foregoing analysis, this research report proposes the following recommendations for policy decision-makers:
Pillar One: Build a System of Mutual Recognition Agreements (MRAs)
China and the EU should, in key areas such as industrial robots, medical diagnostic assistance, and autonomous driving, promote the establishment of mutual recognition lists for AI products and common testing and evaluation standards. Specific pathways include: establishing a China-EU Technical Committee, issuing a cooperation roadmap, forming a joint expert working group to guide pilot cooperation in specific areas, and gradually aligning Chinese regulatory rules with EU standards through assessment and adjustment. The core goal of this MRA system is to prevent enterprises from facing dual regulatory burdens, thereby reducing both the cost for Chinese companies to go global and the digitalization threshold for European companies.
Currently, there is room for alignment between the Brussels Effect of EU AI governance and the flexible adjustment of Chinese regulatory rules. Both sides can provide more public goods at the International Organization for Standardization and other global multilateral forums, offering viable technical reference frameworks for developing countries.
Pillar Two: Establish a Joint Mechanism for Tackling “Global AI Long-tail Challenges”
For common technical challenges in AI implementation – such as few-shot learning, algorithmic bias governance, AI energy efficiency optimization, and computing cost control – China and the EU should establish a dedicated joint research and development fund and a regular cooperation mechanism. The core logic of this mechanism is to "utilize Europe’s strengths in basic mathematics and China’s advantages in computing power scale" to jointly overcome the "computing power anxiety" and "energy costs" hindering AI development.
On an operational level, both sides could promote the establishment of a "China-EU AI Patent Pool," encouraging voluntary sharing of core patents by enterprises. They could establish an open-source whitelist system, supporting universities and research institutions to share code and data in specific fields. They could co-establish national-level joint laboratories for deep collaboration in areas of mutual interest such as Green AI, Medical AI, and Industrial AI. Through full-chain "research-development-application" collaboration, the cross-border flow and optimal allocation of each side's advantageous factors can be accelerated.
Pillar Three: Develop a Normalized Closed-loop “China-EU AI Governance Observatory”
Establish a semi-annual "China-EU AI Governance Closed Door Forum" involving government officials, corporate CTOs, and ethicists from both sides. The forum should focus on "case reviews" of issues such as copyright, privacy, job displacement, and algorithmic discrimination in AI applications. The function of this mechanism is not just information exchange but "policy learning" – through in-depth analysis of specific cases, identifying tensions between governance rules and practical applications, and providing an empirical basis for dynamic rule adjustment.
This "Governance Observatory" should have the following characteristics: First, focus on operational, "technical" issues to avoid premature politicization. Second, involve third-party neutral institutions (e.g., university think tanks, standards organizations) in assessments to ensure professionalism. Third, produce policy briefings and industry guidelines for the public to enhance transparency and predictability. Through this mechanism, China and Europe can aspire to provide the world with a third-party governance reference that transcends the "great power game" framework, truly playing a leading role as a "responsible technology community."
6. Conclusion: Towards a Responsible Technology Community
The fundamental differences in China-EU AI development should not be simplistically framed as a "democracy versus authoritarianism" narrative, nor reduced to a "who leads, who lags" technology race. The real question is whether two institutional systems and technological paths, each with their own strengths, can, through institutional mutual learning and pragmatic cooperation, jointly tame this technology that is profoundly reshaping human society.
This study’s core judgment is that deepening China-EU AI cooperation is not only economically rational but also strategically necessary. For China, the EU’s high-standard regulations and foundational research strength are important references for enhancing the internationalization of its AI industry and upgrading its governance capacity. For the EU, China’s large-scale application scenarios and rapid iteration capability represent a realistic foundation for alleviating anxiety over "lack of innovation" and achieving the goal of "technological sovereignty." The high degree of alignment between both sides on sustainable development issues – green transition, aging populations, healthcare – provides a solid agenda basis for technological collaboration.
However, cooperation will not happen automatically. Transcending the zero-sum mindset requires genuine strategic vision from policymakers on both sides – depoliticizing technological progress and returning to the pragmatic essence of technical collaboration. An open, rules-oriented, technologically interconnected China-EU cooperation framework can not only alleviate Western anxieties about technological runaway but also provide a stable environment for the long-term internationalization of China’s AI industry.
As the message from the CEIBS forum conveyed: The landscape of EU-China relations in the next fifty years depends on "the ability to turn challenges into collaboration." In the critical field of AI, which concerns the common fate of humanity, China and Europe have both the capability and the responsibility to forge a new path that transcends competition and achieves coexistence.
References
[2] When the "Experienced Chinese Driver" Meets German Right-of-Way: How Does ZhiJia Xincheng Win Overseas Orders? Cyzone, 2026-05-09.
[3] Lu Chuanying. EU AI Strategy and Prospects for China-EU AI Governance Cooperation. Center for Strategic and Security Studies, Tsinghua University, 2025-06-17.
[4] Dialogue with CEIBS’s Fang Yue: China-EU Cooperation Is Not About "Who Is Stronger"; AI Applications Need to "Complement Each Other’s Strengths." Phoenix Weekly, 2026-05-08.
[5] Zoyte Technology Establishes European Headquarters in Braunschweig: What’s the Logic Behind the Localization Strategy? News18a, 2026-05-30.
[6] Regulation Too Strict, Innovation Too Slow: Does European AI Need a Dose of "Chinese Medicine"? China Daily China Watch Think Tank, 2025-06-26.