Artificial Intelligence in the Future: Europes Lost Race for Sovereignty?

- tsp
Last update 23 Jul 2025
Reading time 11 mins

Artificial Intelligence (AI) is poised to become the defining technology of the 21st century. It has the power to transform economies, disrupt labor markets, rewrite how fast science can progress and reshape the geopolitical landscape. While countries like the United States and China are racing ahead with enormous investments in AI infrastructure, Europe seems to be caught in a paradox: highly vocal about regulating AI but lethargic when it comes to enabling its development.

In this (not completly objective opinion based) article, we critically examine the current landscape of global AI development, compare the infrastructural and political approaches of major players and highlight the looming risks of Europes stagnation in this arena.

The Global AI Arms Race: Infrastructure First
- United States
- China
Europes Contradictory Approach: Regulations Without Foundations
Where Innovation Happens: The Role of Startups and Hackers
Environmental Concerns vs. Strategic Competence
A Path Forward: What Should Change?
Conclusion
References

The Global AI Arms Race: Infrastructure First

United States

The United States maintains a stronghold on the AI frontier through a combination of several key factors. Industry leadership is a major driver, with tech giants like NVIDIA, Google, Microsoft, Meta and OpenAI leading both hardware and software innovation. In terms of hardware, the US dominates the design of GPUs, TPUs and NPUs particularly through companies like NVIDIA and AMD, although much of the manufacturing is outsourced to Taiwans TSMC.

The availability of vast training data also contributes significantly to this leadership, with platforms like YouTube, Reddit and Instagram offering enormous pools of multilingual and multimodal content for training.

Finally, the synergy between public and private sectors plays a critical role, as government funding programs such as those from DARPA and the NSF are closely aligned with private research and defense-oriented AI applications, creating a reinforcing cycle of innovation.

China

Chinas strategy, while distinct from that of the United States, is no less ambitious in scale or intent. The Chinese government has taken a highly proactive role, pouring billions into AI research under initiatives like the “Next Generation AI Development Plan.” This top-down involvement has resulted in coordinated national efforts that few other countries can match.

China also benefits from a significant domestic data advantage. With a vast population and comparatively lenient privacy restrictions, the country generates massive quantities of usable data. This data serves as a powerful fuel source for training AI models across language, vision and behavioral domains.

Although still reliant on Western semiconductor manufacturing tools, China is aggressively pursuing its own hardware capabilities. Companies such as Huawei and Biren are developing domestic GPU and TPU solutions aimed at reducing dependence on foreign technology.

Additionally, China’s centralized state infrastructure provides an environment where AI systems can be implemented at a massive scale, such as in city-wide surveillance networks. This ability to rapidly deploy and iterate on AI technologies across society gives China a formidable edge in turning research into applied power.

Europes Contradictory Approach: Regulations Without Foundations

In stark contrast to the infrastructural urgency seen in the US and China, the European Union has chosen a path characterized by heavy regulation and limited practical empowerment. The AI Act, for instance, is deeply focused on defining risk categories, forbidding certain applications and placing strict boundaries on what training data can be used - creating a chilling effect not just on corporate actors, but on individual innovators and academic researchers alike.

This environment has left Europe with low sovereignty in AI development. Most of the training of frontier models still happens outside its borders, carried out by institutions like OpenAI, Anthropic and Google DeepMind. Even basic hardware independence is lacking: Europe has no major domestic GPU or NPU producers and remains reliant on American-designed chips and Taiwanese fabrication, or on renting compute time from foreign cloud services (even though some providers actually transfer hardware to Europe they still even have legal limitations on what they can provide that is dictated by foreign governments - like GPU usage and export limitations for different European countries - even different for different EU countries).

Environmental debates have further distracted from the core infrastructural shortcomings. Instead of pushing forward with greener but scalable AI infrastructure, the focus often shifts to whether AI should exist at all due to its power consumption - a framing that does little to advance European capabilities.

Efforts to make up for this structural gap frequently fall into the trap of symbolic action. Funding is funneled into small-scale projects that have little chance of scaling, often handed to companies without meaningful ambitions in AI hardware or foundational models. Partnerships tend to favor entrenched telecoms or national infrastructure players whose sluggishness and risk aversion make them poor champions for a dynamic field like AI. Meanwhile, academic labs are left to study models trained overseas, without the hardware needed to contribute at the cutting edge.

Perhaps most worrying is that the regulatory overhead has grown so severe that truly innovative individuals - the lone hackers, passionate students and daring startups that once launched revolutions from garages and dorm rooms - can no longer meaningfully participate. The legal uncertainty around data use, the opaque and risk-laden AI laws and the lack of accessible compute power together form a gauntlet that only the most well-funded actors can navigate. Europe, in its effort to govern AI responsibly, risks regulating its own future out of existence.

Where Innovation Happens: The Role of Startups and Hackers

Historically, the greatest technological leaps especially in the information technology sector did not come from giant corporations or state-funded behemoths, but from passionate individuals and small teams willing to challenge the status quo. The personal computer revolution was ignited in garages by homebrew hackers with little more than soldering irons and vision. The backbone of todays internet - open-source software like Linux and Python - was built by university students and independent developers. The early days of the web werent shaped by telecom monopolies, but by nimble startups experimenting with new ways to share and connect and hardware manufacturers providing open access to their hardware and open documentation.

Artificial Intelligence could follow the same trajectory. It is a field still ripe with potential for small, daring actors to invent something that changes everything. But in Europe, such a path is increasingly blocked. The very people who once would have driven the next breakthrough - curious individuals, startups brimming with ideas, research collectives operating outside institutional silos - find themselves shut out by legal and infrastructural barriers. Access to GPU clusters, essential for training modern models, is nearly impossible without institutional backing. Even those who manage to collect interesting datasets often run into vague legal frameworks that leave them in constant fear of violating GDPR or AI-specific compliance rules.

What’s more, the critical problem of training data acquisition remains far out of reach for these smaller actors. While corporations like Google, Meta, Microsoft and Amazon have spent the last three to four decades amassing vast reservoirs of user interactions, media content, knowledge bases and behavioral metadata as well as hardware and network infrastructure, Europe has no equivalent treasure trove. Catching up would require a massive, coordinated effort to build open and accessible (mostly annotated) data pools - resources that must be made available to small teams and independent developers alike if we are truly serious about inclusive and sovereign AI development. Without such a foundation, the dream of bottom-up innovation remains a hollow promise.

To make matters worse, deploying an AI service in Europe feels like navigating a minefield. The legal risk is so high and the laws so opaque that only the most well-funded companies with dedicated legal teams dare to try. Rather than creating an environment where innovation flourishes, Europes regulation-first mentality and slow-moving public funding systems have created a gated community of privilege. Those outside the gates - no matter how brilliant - are left shouting from the margins. In trying to control the risks of AI, we may have also locked out its future inventors.

Environmental Concerns vs. Strategic Competence

Yes, AI consumes significant energy. But so do the everyday activities of modern life that we rarely question - commuting to work in cars and trains, flying for business or vacation, running household appliances like washing machines and dryers or maintaining comfort through heating and air conditioning. These routines contribute far more to our environmental footprint than most people realize.

While other countries are actively investing in solutions to the energy challenge - expanding nuclear energy, developing resilient renewable infrastructure and optimizing data centers - Europe often defaults to the idea of restricting or banning emerging technologies like AI under the premise of reducing energy use. Its a reactive strategy rather than a visionary one. Historically, cheap and abundant energy has been one of the strongest drivers of innovation and it consistently correlates with higher living standards, economic resilience and individual well-being. By aiming primarily to reduce energy use instead of securing affordable, sustainable supply for the technologies of tomorrow, Europe risks sacrificing its ability to compete, adapt and thrive in a world where others are racing ahead.

The real risk is not in energy consumption itself, but in how we choose to respond. While others increase their technological and geopolitical leverage by embracing AI and planning energy policies accordingly, Europe risks becoming a passive consumer and policy watchdog for systems it neither controls nor fully understands.

A Path Forward: What Should Change?

To remain relevant, Europe must invest in hardware sovereignty by supporting European chip design and alternative fabs. But this is not a short-term challenge that can be solved within a single legislation period - it is a long-term commitment that will require persistence over one or even several decades. Building the foundational infrastructure for AI, particularly at the hardware level, takes time, strategy and sustained political and industrial alignment. And the first few years it will cost massive amounts of money.

At the same time, Europe holds a unique advantage that its global competitors often lack: a rich tradition and strong ecosystem around open-source software and hardware. This cultural and technical asset could serve as the foundation for a distinctively European approach to AI - one that empowers collaboration, transparency and accessibility. By embracing and amplifying open-source initiatives, Europe could foster an inclusive development model where small players, researchers and even hobbyists are not excluded but actively encouraged to contribute.

To make this possible, Europe must enable small players by creating public compute clusters accessible to startups, researchers and citizens in very simple ways. Legal frameworks must be clarified to remove ambiguity from data usage laws, reduce the amount of legal and administrative work massively and encourage innovation. Regulatory overreach must be scaled back to avoid killing open-source development and non-commercial research. Laws should be tied to the actual capabilities and infrastructures available within Europe rather than idealistic abstractions. And above all, AI must be seen as a tool to fight problems like climate change and build resilience - not as a threat to be banned.

Conclusion

The trajectory of artificial intelligence will not only redefine global economic and cultural landscapes, but also determine the sovereignty of nations. Europe, with its intellectual wealth, democratic values and deep scientific traditions, has every ingredient it needs to become a leader in this space. Yet it continues to falter - not due to a lack of talent or vision, but because it has failed to construct the environment in which such talent can thrive. When infrastructure is rented from abroad, when legal frameworks paralyze experimentation and when the hands of individual innovators are tied by bureaucracy, the dream of a sovereign AI future slips further away.

Europes current posture risks relegating it to a mere observer - a continent rich in theory but poor in execution, watching from the sidelines as others write the protocols, train the models and claim the power. If we do not change course - boldly, strategically and soon - we may find that the future of AI and the systems it governs, are no longer ours to shape.