Europe's strategic path in AI development: Pragmatism instead of technology race – Commentary on Eva Maydell (Member of the European Parliament)

Reality instead of a race – Gigafactories instead of hype: The pragmatic AI path that sets Europe apart from the USA and China

The debate about Europe's role in the global race for artificial intelligence has taken a significant turn with the statement by Bulgarian MEP Eva Maydell . Her position illustrates a strategic approach that positions Europe not as a laggard in the technology race, but as a pioneer of practical and value-driven AI development.

Eva Maydell's vision for Europe

Eva Maydell, who played a key role in European AI policy as one of the leading negotiators of the EU AI Act and the Chips Act, represents a nuanced position on the European AI strategy. As a member of the European Parliament's Committee on Industry, Research and Energy and a recognized expert in digital innovation, she brings years of experience in technology policy.

Their core thesis is both pragmatic and visionary: Europe should not pursue the illusory goal of developing a European alternative to ChatGPT or winning the race for superintelligence. Instead, the continent should focus on developing AI tools that can actually be used by European companies and industries and that deliver measurable benefits.

According to Eva Maydell, our focus should be on:

• Develop specialized niche AI ​​models that specifically meet business needs – rather than participating in the global race.
• Building a robust infrastructure with powerful computing capacity, stable connectivity, and human-centered AI frameworks.
• Ensure that AI remains a tool – it should support humans, not replace them.

The reality of AI adoption in Europe

Current figures on AI usage in Europe clearly support Maydell's argument. Despite the media hype surrounding artificial intelligence, the data paint a sobering picture: only 13.5 percent of European companies have adopted at least one AI technology by 2024. This low adoption rate reveals a significant gap between technological capabilities and practical implementation in business.

This discrepancy becomes particularly evident when looking at company size. While large companies with over 250 employees achieve an adoption rate of over 40 percent, only approximately 12 percent of small and medium-sized enterprises use AI technologies. These figures are particularly relevant given that small and medium-sized enterprises form the backbone of the European economy, accounting for 90 percent of all companies in Europe.

The sectoral distribution of AI usage reveals further interesting patterns. The information and communications sector leads with an adoption rate of 48.7 percent, followed by professional, scientific, and technical services at 30.5 percent. In all other economic sectors, the usage rate is well below 16 percent, highlighting the limited penetration of AI technologies in the broader economy.

Obstacles to AI adoption

The reasons for the hesitancy in AI adoption are diverse and systematic. Companies face significant barriers that hamper successful implementation. The complexity and high costs of AI implementation pose insurmountable hurdles, especially for smaller companies.

A shortage of qualified specialists in the AI ​​field further complicates the situation. Many companies lack the necessary know-how to successfully implement and operate AI systems. Furthermore, there is often a lack of clear use cases that demonstrate the concrete benefits of AI for specific business processes.

Regulatory uncertainty, particularly regarding the implementation of the EU AI law, is also contributing to the reluctance to adopt AI. Companies are hesitant to invest in technologies whose regulatory frameworks are not yet fully clarified.

The European approach: specialization instead of generalization

Maydell's proposal to focus on niche AI ​​models corresponds to the strengths and needs of the European economy. Instead of trying to compete with the major technology companies from the US and China in the field of general AI, Europe should leverage its industrial expertise and regulatory know-how to develop specialized AI solutions.

This strategy offers several advantages. Specialized AI models require significantly less computing power and investment than general-purpose models, making them more accessible to European companies. At the same time, they can be more precisely tailored to the specific needs of specific industries and use cases.

The European market offers numerous opportunities for such specialized applications. In areas such as precision agriculture, automotive repair, healthcare, and manufacturing, AI solutions can be developed that solve concrete problems and deliver measurable improvements.

Infrastructure and computing capacities

A central component of the European AI strategy is the development of a robust infrastructure. The AI ​​Continent Action Plan, presented in April 2025, envisions the creation of a network of AI factories based on Europe's leading supercomputers. These factories are intended to support EU-based AI startups, industry, and researchers in the development of AI models and applications.

The planned AI gigafactories, equipped with approximately 100,000 state-of-the-art AI chips, are expected to quadruple current chip capacity. These facilities will not only enable the development of complex AI models but also strengthen Europe's strategic autonomy in key industrial and scientific fields.

The InvestAI initiative aims to mobilize €20 billion in private investment to build up to five AI gigafactories across the EU. In parallel, a Cloud and AI Development Act is proposed to stimulate private investment in cloud computing and data centers.

Human-centered AI development

A key aspect of Maydell's vision is the emphasis on human-centered AI development. It emphasizes that AI should be a tool that serves humans, not replaces them. This philosophy reflects European values ​​and differs significantly from other approaches primarily focused on technological dominance.

Specifically, the human-centered approach means that AI systems should be developed to complement and enhance human capabilities rather than replace them. This requires careful design of human-machine interactions and ensuring that humans always retain control over key decisions.

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Regulatory framework and trust

The EU AI Act, in whose development Maydell played a key role, embodies the European approach to trustworthy AI. The law is based on a risk-based approach that classifies AI systems into four categories: unacceptable risk, high risk, limited risk, and minimal risk.

This differentiated approach makes it possible to promote innovation while ensuring appropriate safeguards. AI systems with unacceptable risk, such as social assessment or cognitive behavioral engineering technologies, are completely prohibited. High-risk systems are subject to strict requirements, including risk management, transparency, and human oversight.

Implementing the AI ​​Act requires close cooperation between companies, regulators, and other stakeholders. Clear definitions and examples of AI risk categories are essential for companies to comply with the regulations.

Education and skills development

A critical success factor for Europe's AI strategy is the development of relevant skills among the population and the workforce. Maydell emphasizes the need to make AI literacy a basic skill, comparable to reading, writing, and arithmetic.

Most young Europeans use AI every day, but few learn how it works, what risks it poses, or how to influence its development. This education gap must be closed to ensure the next generation has the skills needed for an AI-driven future.

The planned AI Skills Academy, Talent Pool, and MSCA Choose Europe programs are all designed to attract world-class AI professionals to Europe while upskilling local talent in areas such as generative AI. These efforts will not only reverse brain drain but also create legal migration pathways for non-EU experts.

Economic prospects and productivity increases

Research by the McKinsey Global Institute estimates that generative AI could help Europe achieve an annual productivity growth rate of up to 3 percent by 2030. This forecast underscores the enormous economic potential of AI technologies if successfully implemented.

Recent studies already show positive effects of AI adoption on productivity. Ninety percent of European AI users report productivity increases, and 75 percent say that AI has changed the way they work. These results demonstrate the practical potential of AI technologies beyond the technological hype.

Successful AI adoption is particularly important for small and medium-sized enterprises, which form the backbone of the European economy. Studies show that 39 percent of SMEs now use AI applications, an increase from 26 percent in 2024. Specifically, 26 percent use generative AI, an increase from 18 percent in the previous year.

Sector-specific applications

The focus on sector-specific AI applications reflects Europe's strengths in various industries. In healthcare, AI systems can help improve diagnoses and personalize treatments. In manufacturing, they can optimize production processes and improve quality control.

AI offers unique opportunities in the field of sustainable development and climate protection. German initiatives such as the "AI Lighthouses for Environment, Climate, Nature, and Resources" demonstrate how AI can be used to solve environmental problems. With funding of €40 million, the program supports application-oriented research projects in areas such as energy efficiency, resource efficiency, and biodiversity conservation.

International cooperation and strategic partnerships

Europe's AI strategy benefits from international cooperation, especially with like-minded democratic partners. Maydell's role in the Delegation for Relations with Japan and the US underscores the importance of such partnerships. These collaborations enable the exchange of best practices, the joint development of standards, and the coordination of regulatory approaches.

Collaboration is especially important given the global nature of AI development and deployment. European startups are often forced to collaborate with US technology giants to gain access to the necessary complementary services. Instead of hindering these collaborations, Europe should facilitate them while simultaneously building its own capabilities.

Data access and quality

A key component of successful AI applications is access to high-quality data. The planned Data Union strategy, to be launched in 2025, will support these efforts by establishing a single market for data. This will make it easier for companies and researchers to scale AI solutions across borders while respecting EU data protection standards.

Data labs within AI factories are designed to collect and curate datasets from various sources, thus creating the foundation for AI training and experimentation. This infrastructure will be particularly important for the development of specialized AI models that rely on high-quality, domain-specific data.

Financing and investments

Financing AI innovation remains a key challenge for Europe. The continent is experiencing a funding gap in AI investments that requires improved access to finance, better venture capital support, and strengthened public-private partnerships.

The InvestAI initiative and other financing mechanisms aim to close this gap. At the same time, it is important to promote the development of European venture capital and private equity markets to create sustainable sources of financing for AI startups.

AI Gigafactories vs. Efficient Mini-Models: Europe's Strategic Dilemma

Despite the ambitious plans, Europe's AI strategy faces significant challenges. Critics argue that the focus on building massive computing infrastructure through AI gigafactories may not align with emerging trends toward smaller, cost-effective AI models. European startups, inspired by the success of DeepSeek, are already implementing training techniques that achieve efficiency without extensive computing power.

The regulatory complexity of the planned AI law could hamper innovation. Overly broad definitions of "high-risk" and "general-purpose AI" could slow down European companies and research institutions—especially in light of intense global competition.

Maydell's vision: Value-oriented AI for citizens and businesses

The future of European AI development depends on successfully balancing innovation and regulation. Maydell's vision of pragmatic, value-driven AI development offers a guide for this balancing act. Europe can certainly succeed without winning the global AI race by creating technologies that are accessible, implementable, and transparent, and consistent with the continent's democratic values.

Success will ultimately be measured by whether European companies and citizens can benefit from AI developments. This requires continuous adaptation of strategies to changing technological and economic conditions, as well as close cooperation among all stakeholders.

The coming years will be crucial for realizing this vision. Europe faces the challenge of using its regulatory leadership to develop a new form of technological sovereignty—one based not on raw computing power or market dominance, but on the ability to develop and deploy AI technologies that serve people and advance societal progress.

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