Europe's AI catch-up: A dedicated AI industry with the “Apply AI strategy” – Between sovereignty and competitive reality

1. A late course correction in turbulent times

The European Union is at a critical juncture in its digital history. While it has served as a regulatory pioneer in the field of artificial intelligence for years, there is a growing realization that a purely regulatory approach is not enough to survive in the global AI race. In October 2025, the European Commission presented its new "Apply AI Strategy," which marks a fundamental paradigm shift: Instead of simply regulating, Europe now finally wants to go on the offensive and build its own AI industry.

The strategy comes at a time when Europe's dependence on American and Chinese technologies has reached dramatic proportions. More than three-quarters of listed European companies rely on US cloud services, and Europe lags significantly behind in critical AI technologies. While the US leads the field in quantum computing and artificial intelligence, and China is catching up rapidly in semiconductors, Europe ranks a distant third in all three key technologies.

The new strategy is based on a painful realization: Europe has largely missed the digital revolution of the past two decades and now risks falling behind in AI as well. With one billion euros from existing programs, the EU Commission intends to promote the use of European AI solutions in eight strategic sectors – from healthcare and energy to defense and the automotive industry. The stated goal is to move beyond the position of a "digital colony" and achieve technological sovereignty.

2. From regulator to laggard: Europe's digital odyssey

The origins of Europe's AI strategy date back to a time when digital transformation was still in its infancy. As early as the 2000s, the EU recognized the importance of digital technologies, but focused primarily on establishing a legal framework. While American companies like Google, Amazon, and Microsoft expanded their market dominance and Chinese corporations like Alibaba and Tencent grew rapidly, Europe focused on regulation and data protection.

The decisive turning point came with the adoption of the General Data Protection Regulation (GDPR) in 2018, which established Europe as a global standard-setter. This success story was repeated with the AI ​​Act, which came into force in 2024 as the world's first comprehensive AI law. The AI ​​Act's risk-based approach classifies AI systems into different categories and subjects high-risk applications to strict requirements – from transparency requirements to human oversight.

But the focus on regulation came at a price. While Europe was writing laws, other continents were building companies. Mario Draghi's September 2024 report bluntly summed up this misery: Europe must become significantly more innovative to compete with the US and China. The EU is trapped in a static industrial structure in which few new companies are emerging to transform existing industries or develop new growth engines.

The numbers speak for themselves: Only four of the world's 50 largest technology companies are European. In AI investments, 61 percent of the machine learning models classified as "notable" worldwide come from the US, followed by the EU with 21 percent and China with 15 percent. In 2024, the EU invested only 6 percent of the global AI startup funding of over $35 billion. These sobering realities led to a rethink in Brussels: Regulation alone is not enough – Europe needs an industrial policy response to the AI ​​challenge.

3. The building blocks of the new AI offensive

The EU's new Apply AI strategy rests on several strategic pillars that together aim to create a coherent ecosystem for European AI innovation. At its core is the transformation of the existing 151 European Digital Innovation Hubs (EDIHs) into specialized "experience centers for AI." These hubs are intended to provide small and medium-sized enterprises with privileged access to the EU AI innovation ecosystem and help bridge the digital divide between large corporations and SMEs.

The heart of the technical infrastructure will be the AI ​​factories and planned gigafactories. The EU has already selected 19 sites for AI factories and is planning six more in the Czech Republic, Lithuania, Poland, Romania, Spain, and the Netherlands. These facilities will provide startups, SMEs, and industry with direct access to AI-optimized supercomputers. Investments total over €500 million for the new sites alone, with the more ambitious gigafactories planned with a total volume of €20 billion.

A key component is the newly created Apply AI Alliance, a coordination forum bringing together industry, the public sector, academia, social partners, and civil society. This alliance will serve as a central interface between AI stakeholders and the Commission and advance the dialogue on AI policy in strategic EU sectors. In parallel, the Frontier AI Initiative will be launched, bringing together Europe's leading industrial and academic players to accelerate progress in frontier AI capabilities.

The strategy identifies eight priority sectors for AI implementation: healthcare and pharmaceuticals, mobility and transport, robotics, manufacturing and engineering, climate and environment, energy, agriculture and food, and defense and security. In healthcare in particular, the EU is focusing on concrete applications such as AI-supported screening centers that should enable more accurate diagnoses using imaging techniques. In science, the virtual European institute RAISE is being created to pool AI resources for the development and application of AI in research.

4. Between ambitions and realities

The implementation of the Apply AI strategy is taking place in a challenging environment characterized by geopolitical tensions and technological dependencies. Currently, only 37 percent of German companies use AI technologies, with large companies being significantly more active at 66 percent than small businesses at 36 percent. Across Europe, AI use is only 13.5 percent of companies, while the EU aims to achieve a rate of 75 percent by 2030.

The greatest challenge lies in the structural dependence on foreign technologies. Approximately 75 percent of European companies rely on American cloud providers, and US and Asian suppliers dominate the market for critical AI components such as semiconductors and AI chips. This dependence is exacerbated by geopolitical developments: With its AI Action Plan strategy, the Trump administration has formulated the goal of achieving "global technological dominance" and making allies structurally dependent on US technology.

Europe faces the dilemma of having to implement its AI strategy in a market already dominated by others. Even promising European AI companies like France's Mistral rely on foreign suppliers for hardware, software, and critical minerals. Mistral, considered a beacon of hope for European large language models, is valued at just under €12 billion, while direct US competitors like OpenAI, Anthropic, and xAI are estimated at hundreds of billions of dollars.

The regulatory framework, which Europe touts as a strength, is increasingly perceived by industry as a barrier to innovation. Critics describe the AI ​​Act as a "bureaucratic monster" that imposes high compliance costs, especially on small and medium-sized enterprises. Legal experts speak of "absolute compliance overkill" for high-risk AI applications that could stifle innovation. This criticism is reinforced by the fact that only 11 percent of the 383 recommendations from the Draghi report have been implemented so far.

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5. Success stories and learning examples from practice

Despite the structural challenges, there are already remarkable examples of successful AI implementation in European companies, demonstrating what is possible when the right framework is created. Siemens AG has developed its Digital Lighthouse factory in Erlangen into a prime example of industrial AI applications. By using AI, digital twins, and robotics in over 100 use cases, the company achieved a 69 percent productivity increase and 42 percent energy savings over four years.

Another impressive example is Zalando, which offers 29 million customers a personalized shopping experience with its "Algorithmic Fashion Companion." The digital outfit recommendation tool is powered by AI and machine learning and optimizes not only the customer experience but also internal processes such as supply chains and fraud prevention. Especially during Cyber ​​Week, AI enables the company to create an exemplary customer experience with flexible payment and delivery options, as well as week-specific discounts.

In the smaller business sector, the example of Kaput Podcasts demonstrates how generative AI technologies can revolutionize creative processes. The company was able to reduce the time spent on repetitive tasks in podcast production by 75 percent without compromising quality. This case study demonstrates the enormous potential of AI for content creation and media production—areas in which Europe is traditionally strong.

These success stories also highlight the strategic advantages Europe can leverage in the AI ​​competition. Unlike pure software applications, Europe possesses deep industrial know-how and high-quality domain data. By embedding this expertise in AI-supported applications in areas such as operations, procurement, or finance, European complexity can be transformed into European competitiveness. Especially with tabular models trained on structured data, manufacturers can leverage their data efficiently—an advantage that is especially valuable where auditability is essential.

6. Structural obstacles and systemic weaknesses

The implementation of the European AI strategy is hampered by a number of structural problems deeply rooted in the DNA of the European innovation ecosystem. The most serious problem is the lack of complementary markets necessary for a successful AI business. Europe lacks large-scale business customers for frontier generative AI models that could generate sufficient revenue to cover the enormous fixed costs of model training. Likewise, hyperscale cloud computing infrastructures and private equity financing for European-scale AI startups are lacking.

The costs of catching up with leading Big Tech AI computing centers are already prohibitive for EU budgets and are expected to rise further. While the EU is focusing on expanding an existing supercomputer network with more AI hardware, this computing infrastructure is not adapted for AI modeling. This focus on hardware overlooks the lack of EU markets for complementary services necessary to build a successful AI business.

Another systemic problem lies in the fragmented structure of the European single market. Despite theoretical harmonization, companies still have to contend with different national implementations and bureaucratic hurdles in practice. This fragmentation is further exacerbated by the AI ​​Act, as different member states may develop different interpretations of the regulations. Duplicate regulations between the AI ​​Act, the Data Act, and the GDPR create additional complexity that can be particularly overwhelming for smaller companies.

The EU-US trade agreement further entrenches dependence on foreign technologies. While Europe imports more than €300 billion worth of digital services from the US annually, the EU continues to fail to impose a uniform digital tax on the sales of US tech giants in the European market. At the same time, the agreement directs significant investments from Europe to US industry, at the expense of building European capacity. The situation is exacerbated by the erratic policies of the Trump administration, which treats Europe as a potential "data colony" and seeks to promote digital imperialism by exporting the entire American AI stack.

7. Scenarios for the European AI future

The future of the European AI strategy depends on various factors that could play out in different scenarios. In the most optimistic scenario, Europe succeeds in combining its industrial expertise and regulatory competence to create a unique market position. The "Trusted AI" model could establish itself as a global standard, similar to how the GDPR influenced global data protection regulation. In this scenario, European AI solutions would be marketed as particularly trustworthy and ethical, giving them access to sensitive areas such as healthcare and financial services.

A more likely middle scenario sees Europe emerging as a successful "application world champion," not competing in frontier models but becoming a leader in specialized industrial AI applications. In this model, Europe focuses on AI applications below the technology frontier, requiring far less computing power and lower investment costs. By promoting the adoption of AI application services across a broad range of industries, Europe could significantly stimulate productivity growth without entering the prohibitively expensive frontier race.

The more pessimistic scenario sees Europe as a permanent laggard, remaining structurally dependent on American and Chinese technologies. The three global AI strategies—the US frontier race, Europe's regulatory path, and China's ground-based applications—could develop in such a way that Europe finds itself caught between two stools. While the US maintains the technological edge through private investment and bold innovation, and China maximizes practical benefits through state-coordinated mass adoption, Europe's regulatory approach could hinder both innovation and adoption.

Geopolitical developments will be decisive. If the US and China enter a new technological Cold War, Europe could be forced to choose a side or attempt to maintain a neutral position. The "balance between great powers" could well bring Europe advantages if it skillfully navigates between the camps while developing its own technological niches. Alternatively, Europe could also attempt to form a "middle power alliance" with countries like India, Japan, or South Korea to jointly counter the great powers.

8. Turning point or turning point: A critical classification

The European Union's Apply AI strategy undoubtedly marks an important turning point in European technology policy. After years of a primarily regulatory approach, the strategy signals the willingness to finally act as a technological player. The billion euros in funding, the construction of AI factories, and the transformation of Digital Innovation Hubs demonstrate that Europe has recognized its homework.

Nevertheless, considerable doubts remain as to whether these measures will be sufficient to overcome the structural deficits. The financial allocation of one billion euros seems modest compared to the $58.5 billion that the US alone invested in AI venture capital in 2024. Even the more ambitious €20 billion for gigafactories is a fraction of what would be necessary for a true catch-up race. Mario Draghi's call for additional annual investments of €750 to €800 billion clearly demonstrates the dimensions Europe needs to consider.

The biggest challenge lies not in the technology itself, but in the market structures and business models. As long as European AI startups are forced to cooperate with US Big Tech companies to gain access to computing capacity, data, and markets, the dependency will persist. The Apply AI strategy only superficially addresses these fundamental problems and relies too heavily on government intervention in a field primarily driven by private innovation and venture capital.

Europe's best opportunity may not lie in direct competition with the US and China for frontier AI, but in skillfully leveraging its specific strengths. The combination of industrial know-how, high-quality data, and trustworthy regulation could create a unique market position. If Europe succeeds in making AI a standard tool in its traditional areas of strength—from mechanical engineering to the chemical industry to the automotive industry—it could find a profitable niche in the global AI ecosystem.

The Apply AI strategy is a necessary but not sufficient step. It demonstrates that Europe has understood the challenge, but leaves open the question of whether the political will and financial resources are sufficient to turn the vision into reality. The window of opportunity for a successful European AI strategy is rapidly closing—but it is not yet completely closed.

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