Shifting Frontiers: Europe’s AI Strategy in a Changing Technological and Geopolitical Landscape

CSDS POLICY BRIEF • 11/2026

By Antonio Calcara

29.4.2026

Key issues

• Europe should prioritise strategic access to critical AI inputs rather than attempt to replicate the full AI stack.
• AI value creation is shifting from frontier models towards scalable and commercially viable applications.
• Europe’s competitive advantage lies in trusted, sector-specific AI applications built on strong regulation and high-quality data.

Introduction

In September 2025, in a CSDS Policy Brief co-authored with Riccardo Bosticco, we argued that Europe should tailor its artificial intelligence (AI) strategy to the rapidly evolving conditions of the global AI market and technological landscape. In Shifting Grounds? On the Need to Rethink Europe’s AI Ambitions and Competitiveness, we identified two structural trends shaping the trajectory of AI. First, declining computing costs were progressively lowering barriers to entry. Second, value creation was shifting away from infrastructure development towards business applications and end-user solutions. As a result, we contended that competition in AI would increasingly hinge not on building infrastructure alone, but on leveraging it to enhance productivity and generate economic value. Encouragingly, recent European initiatives – most notably the Apply AI Strategy – suggest that policymakers are moving in this direction. Yet, significant room for improvement remains.

This CSDS Policy Brief revisits and expands upon these trends by examining recent technological and market developments across both the hardware and software layers of the AI stack. The overall objective here is to align Europe’s technological strategy with existing dynamics while anticipating future transformations. On the hardware side, we analyse developments in data centres and semiconductor supply chains, with particular attention to bottlenecks in memory chips and their geopolitical and economic implications. On the software side, we explore the emerging limits of large language models (LLMs), the growing imperative to monetise AI and the market’s shift towards scalable and commercially viable applications.

Taking stock of these transformations, the policy brief assesses Europe’s current policy trajectory and outlines what the European Union (EU) should prioritise to remain competitive. The core argument is that the two trends identified here point towards a more selective and strategic European approach. On the hardware side, the growing importance of memory bottlenecks, globally integrated supply chains and trusted partners suggests that Europe should not aim to replicate the entire AI stack, but rather secure access to critical inputs through targeted industrial policy and external partnerships. On the software side, the maturation of frontier models and the shift of value creation towards applications suggest that Europe’s greatest opportunity lies in building high-impact, sector-specific uses of AI. The policy brief develops this argument through the case of healthcare, a sector in which Europe’s regulatory strengths, industrial capabilities and high-quality datasets can be translated into both innovation and public goods.

The hardware of AI

Data centres and semiconductor production are rapidly emerging as the backbone of the global AI economy, reshaping trade patterns, investment flows and technological competition. The surge in AI-related investment has become a major driver of global economic activity. The World Trade Organisation estimates that AI-driven demand accounted for nearly half of global goods trade growth in 2026, underscoring the strategic importance of digital infrastructure. This expansion is geographically concentrated: North America accounts for more than half of the world’s data centre build-out, consolidating the United States’ (US) leadership in the computational foundations of AI. As the physical infrastructure of the digital economy, data centres have become key strategic assets, anchoring both economic competitiveness and geopolitical influence.

Despite increasing geopolitical rivalry, AI infrastructure remains deeply embedded in global value chains. Investments in AI exhibit a high import intensity – estimated between 70% and 90% – highlighting their dependence on internationally sourced components, advanced manufacturing capabilities and cross-border energy flows. Even the US relies on global supply chains for critical inputs. Among these, memory chips, particularly High Bandwidth Memory (HBM), have emerged as the principal bottleneck in AI development. Essential for training and deploying advanced models, their production is heavily concentrated in East Asia, especially in South Korea. Reflecting the scale of global demand, South Korea’s semiconductor capacity is reportedly sold out for the next two to three years. This concentration exposes structural vulnerabilities in the global AI ecosystem and underscores the strategic importance of secure and resilient supply chains.

Recent geopolitical developments further illustrate the fragility of the hardware underpinning the AI revolution. Data centres are increasingly recognised as strategic infrastructure and, in times of conflict, military targets. At the same time, disruptions to global energy flows risk exacerbating technological bottlenecks. Instability in critical maritime chokepoints, such as the Strait of Hormuz, could threaten energy supplies to East Asia, where much of the world’s semiconductor manufacturing is located. Such disruptions would reverberate across global value chains, increasing costs and constraining production capacity. These interdependencies highlight how access to energy, secure trade routes and geopolitical stability are now integral to the evolution of the AI economy.

Technological innovation may alleviate some of these constraints, yet it is unlikely to eliminate them altogether. Advances in chip design and computational efficiency – such as Google’s recent breakthroughs aimed at reducing memory requirements for AI workloads – have the potential to optimise performance and expand access. However, these efficiency gains may also produce paradoxical effects. As described by the Jevons paradox, improvements in efficiency often lead to increased overall consumption of a resource rather than reduced demand. In the context of AI, more efficient hardware could lower costs and accelerate adoption, thereby intensifying demand for computing power and memory. Consequently, rather than eliminating scarcity, technological progress is likely to amplify global competition for critical AI infrastructure.

The software of AI

Hardware and software are both essential to the development and diffusion of artificial intelligence, but it is increasingly at the software layer – and especially in applications – that firms are trying to capture value and build sustainable business models. In recent years, LLMs have driven remarkable breakthroughs, transforming industries and accelerating digital innovation. However, many analysts now believe that advances in frontier models are becoming more incremental, with diminishing returns relative to their escalating costs. As a result, the real economic opportunity is shifting from building ever more powerful models to embedding them into products and services that generate sustained usage and revenue. Simply developing cutting-edge models is no longer sufficient for commercial success. Instead, the competitive frontier is moving towards AI-powered coding tools, autonomous agents and enterprise applications. This shift reflects both technical and economic constraints, including the scarcity of high-quality training data and the difficulty of establishing sustainable business models for increasingly expensive foundation models.

These challenges have prompted companies to reassess their strategies. Rather than focusing exclusively on scaling frontier models, several firms are turning towards smaller and mid-sized models that balance performance with cost efficiency. This trend reflects the growing recognition that optimisation, specialisation and integration often deliver greater commercial value than sheer scale. Microsoft’s decision to invest in mid-tier AI models illustrates this shift, as compute limitations and cost considerations encourage a move towards more efficient and targeted solutions. Such models are particularly suited for enterprise applications, where reliability, affordability and adaptability matter more than marginal improvements in benchmark performance.

At the same time, the imperative to monetise AI is reshaping business strategies across the industry. Companies are moving from experimentation to revenue generation, prompting a gradual shift from open-source approaches towards proprietary ecosystems. Meta and Alibaba, for instance, have adjusted their strategies to emphasise commercially viable models alongside open initiatives. This reflects a broader industry trend. Indeed, as competition intensifies and development costs soar, firms are seeking to secure returns on investment through vertically integrated platforms and exclusive offerings.

The race for users further illustrates this evolution. Competition between leading firms such as Anthropic and OpenAI highlights the strategic importance of attracting and retaining customers. Increasingly, this competition is not centred only on general-purpose assistants, but on specialised, high-value products for selected customers and sectors, including coding, enterprise workflows and cybersecurity. Recent moves – ranging from strategic partnerships to acquisitions and new distribution channels – demonstrate that the ultimate battleground lies in real-world applications.

Taken together, these developments reinforce a broader structural shift: as software innovation matures, competitive advantage is moving towards the application layer.

The EU Apply AI strategy

The EU has increasingly recognised the importance of AI adoption for economic competitiveness, and the European Commission’s Apply AI Strategy represents a significant step in this direction. Launched in 2025, the strategy explicitly aims to move AI from experimentation to large-scale deployment across key sectors of the economy, with a particular focus on industry and the public sector. It promotes an “AI-first” approach, encouraging organisations to systematically consider AI solutions in decision-making processes, while also supporting technological sovereignty and European solutions. Importantly, the strategy is designed as the implementation pillar of the broader AI policy framework, complementing the AI Continent Action Plan by focusing on real-world adoption rather than infrastructure or regulation alone. This shift is visible in recent data: AI adoption in Europe has increased significantly in recent years, rising from around 7-8% of firms in 2021 to roughly 20% in 2025. However, this progress remains uneven across member states, with countries such as Denmark, Finland and Sweden leading adoption, while others, particularly in Eastern Europe, continue to lag behind.

Beyond geographical disparities, the diffusion of AI within European economies is also highly uneven across firm size and sectors. Large enterprises are significantly more advanced in adopting AI technologies, with more than half already using AI in their operations, compared to a much smaller share of small and medium-sized enterprises (SMEs). This is particularly problematic in the European context, where SMEs constitute the backbone of the economy. Sectoral differences are also pronounced: adoption is highest in information and communication services and in professional services, while more traditional sectors lag behind. At the same time, Europe has seen important developments on the supply side. Companies such as Mistral are investing heavily in AI infrastructure and models, often in partnership with global actors, and a growing share of their revenues is generated within Europe. Nevertheless, large European industrial firms are still not central actors in initiatives such as AI factories, which remain largely oriented towards start-ups and developers rather than large-scale industrial users.

Despite these improvements, the way AI is used across European firms remains relatively narrow and often limited to early-stage applications. The most common uses of AI in 2025 include text mining and generative AI for images, audio and text, with primary applications concentrated in marketing, sales and administrative functions. While these uses are important, they do not yet reflect a deep transformation of core business processes or industrial production. This is closely linked to what can be described as a “pilot purgatory” problem. While a large majority of European companies are experimenting with AI technologies, only a small fraction are successfully scaling these solutions across their core operations. Most firms remain stuck in pilot phases, while only a limited share achieves full integration. This gap is further reflected in labour markets: European workers use AI tools significantly less frequently than their counterparts in the US, indicating a broader lag in organisational adoption and diffusion.

Taken together, these trends suggest that while the Apply AI Strategy is directionally correct, important structural challenges remain. The strategy rightly focuses on adoption, sectoral deployment and real-world use cases, targeting key industries such as healthcare, manufacturing, energy and mobility. However, its effectiveness will ultimately depend on its ability to overcome fragmentation, incentivise large-scale adoption and move European firms beyond experimentation towards systematic integration. In this sense, the main challenge for Europe is no longer recognising the importance of AI, but ensuring that it is used at scale across the economy. This is precisely where the insights from the hardware and software trends discussed above become critical: if value creation is increasingly shifting towards applications, then Europe’s competitiveness will depend on its capacity not just to develop AI, but to deploy it effectively.

Conclusion: what should Europe do?

The technological and market trends outlined in this policy brief carry clear implications for Europe’s AI strategy. On the hardware side, the global AI ecosystem is increasingly shaped by concentrated supply chains, and bottlenecks in critical inputs such as memory chips. At the same time, the build-out of data centres remains heavily concentrated in the hands of hyperscalers, even as the spread of AI applications is likely to increase the importance of lower-latency and more distributed computing architectures. On the software side, the maturation of large language models and the growing pressure to monetise AI are shifting value creation from foundational models towards applications. Together, these trends suggest that Europe’s competitiveness will depend less on replicating the full AI stack and more on combining selective industrial policy in hardware with a much stronger push on data and applications.

Additionally, Europe should adopt a more pragmatic and externally oriented strategy. The market for semiconductors and data centres is global in nature, and the main bottleneck in AI hardware now lies in memory, especially high-bandwidth memory, a segment in which East Asian producers remain central. South Korean firms remain major players in HBM, and tight supply has continued to shape the market, while Japan has doubled down on advanced semiconductor capacity through projects such as Rapidus. The EU should deepen industrial dialogue and strategic co-operation with trusted partners such as South Korea and Japan, aligning AI industrial policy with a broader foreign-policy need to work more closely with reliable partners in a more conflictual geopolitical environment. But Europe should approach these partnerships not only politically, but also commercially: to secure meaningful access to critical chips, European governments and firms will need to aggregate demand and act as credible buyers through public-private partnerships. In parallel, while hyperscalers are likely to dominate large-scale cloud infrastructure for some time, Europe should invest more aggressively in edge computing, where the shift towards applications will create growing demand for lower latency, local storage and compute resources closer to the sectors and products that actually use AI.

On the software side, Europe’s priority should be to act on data and applications. The Apply AI Strategy is going in the right direction, but its success will depend on building a trustworthy and innovation-friendly European data regime. As hardware bottlenecks limit Europe’s ability to compete at the infrastructure layer, its comparative advantage lies in harnessing high-quality data and translating them into impactful applications. Europe’s regulatory power can therefore become a source of competitive strength. The EU should encourage standardised Application Programming Interfaces, open standards and data portability rules so that European firms can switch providers more easily, retain control over their innovations and capture more value within global AI chains. Data trusts and sectoral data commons – from manufacturing to defence – should become a central pillar of European AI policy.

The healthcare sector offers a particularly strong illustration of how Europe can connect these trends to a strategy of innovation and public goods provision. Healthcare is not only a large economic sector; it is also one in which Europe possesses distinctive assets: universal health systems, rich datasets, public legitimacy and a regulatory capacity that can underpin trust. For this reason, healthcare is an ideal domain in which to scale AI applications in diagnostics, drug discovery, hospital operations and administrative efficiency. By accelerating initiatives such as the European Health Data Space, which is designed to create a common framework for the use and exchange of electronic health data across the EU, Europe can enable more secure and effective cross-border data access for research, innovation and public-interest uses.

This approach aligns with both trends identified in this policy brief. First, it mitigates Europe’s relative weakness at the hardware layer by prioritising application-layer innovation in a sector where the Union already has comparative strengths. Second, it captures the shift in value creation from foundational models to real-world use cases. If properly supported through interoperable data infrastructures, public-private partnerships, regulatory clarity and targeted compute capacity, healthcare can become a European model of how AI can generate both competitiveness and public goods. More broadly, this is the kind of strategic leapfrogging Europe should pursue: not trying to copy the AI ecosystems of the US or China in their entirety, but using its own strengths to build trusted, high-value applications in sectors that matter economically, socially and politically.

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