AI’s Global Race and Europe’s Next Moves

Global AI Competitiveness Landscape

Asia – Asia’s tech powers are making massive, coordinated investments to lead the next era. China has a state-driven AI strategy aimed at global leadership by 2030. Beijing’s Next-Generation AI Plan (2017) promotes domestic innovation hubs and AI integration across industries, from smart cities to surveillance, focusing on achieving AI self-sufficiency in critical technologies. Chinese firms deploy AI at scale in everything from e-commerce to autonomous vehicles, backed by the world’s largest internet user base and supportive government policy. Even under semiconductor export curbs, China is racing to build advanced chips and data centers to sustain AI growth. It also leverages international initiatives (the Digital Silk Road) to export AI tech and standards, extending its influence across emerging markets. South Korea, meanwhile, is transitioning from “fast follower” to AI leader status by developing robust infrastructure and talent. Seoul has unveiled plans for one of the world’s largest AI supercomputing data centers (a 3 gigawatt facility in Jeollanam-do costing $35 billion) to fuel a homegrown AI ecosystem. The country’s semiconductor strengths give it an edge – Korean firms control ~90% of the global high-bandwidth memory market, essential for AI hardware. Public-private partnerships are expanding AI R&D, and new laws (like the 2023 Basic AI Act) encourage innovation while addressing ethics.focusing In short, Asia’s priority is building the computing power, skilled workforce, and industrial base needed to dominate AI, often via deliberate national strategies and regional collaborations.

United States – The U.S. enters the AI race with formidable advantages: world-leading tech companies, top research institutions, and deep venture capital pockets. American firms pioneered the AI infrastructure that others now emulate – from cloud computing platforms to cutting-edge AI chips. U.S.-based companies like OpenAI, Google, and Nvidia currently lead in frontier AI models and hardware, reflecting a broader pattern where the U.S. “scaled” key digital innovations into global products. Recent data underscores this dominance: in 2024, U.S. private investment in AI hit $109 billion – 12 times China’s and dwarfing that of any European economy. The U.S. also produced far more “notable” AI systems (40 major models in 2024 alone, vs. 15 from China and only 3 from Europe). This supremacy is enabled by an entrepreneurial ecosystem that rapidly turns research breakthroughs into applications, fueled by talent worldwide. Washington is now mobilizing policies to maintain its edge, from the CHIPS Act (boosting domestic chip fabs) to national AI research funding and new alliances. The U.S. government is also forging partnerships – both public-private (e.g. DARPA initiatives, AI hubs) and international (coordination with allies on AI norms and semiconductor supply chains). While American AI policy emphasizes innovation and light-touch regulation, there’s growing attention to governance (the U.S. co-founded the Global Partnership on AI and convenes G7 discussions on trustworthy AI). Overall, the U.S. prioritizes scaling AI through investment and open collaboration, ensuring it remains at the forefront even as competition intensifies.

Europe – Europe approaches the AI era with a mix of high ambitions and hard lessons from past tech waves. The EU and key states recognize that AI capability is vital for economic competitiveness and digital sovereignty. Europe’s strengths include a top-tier research base and strong industries (automotive, machinery, healthcare) that AI could transform. Indeed, European researchers produced more AI publications than Americans in recent years, and countries like France have nurtured notable AI labs. Yet Europe has struggled to convert this into globally scaled AI platforms or unicorns, and its share of big AI models or private investment remains modest. To change course, European leaders are developing foundational infrastructure and policy. The EU has launched an “AI continent” strategy with plans to build dozens of AI “factories” and several “gigafactories” – essentially clusters of supercomputing data centers to develop next-generation AI models. These facilities (co-funded by a proposed €20 billion investment pool) aim to give European innovators access to world-class computing power on European soil.

Europe is ramping up AI infrastructure: the LUMI supercomputer in Finland (pictured) is one of several high-performance computing centers underpinning the EU’s AI “factory” plans. In parallel, the EU is seeking technological autonomy in critical hardware, rolling out a €43 billion Chips Act to boost semiconductor manufacturing (to produce 20% of the world’s chips by 2030). Policy-wise, Europe has been a trailblazer in AI governance – drafting the world’s first comprehensive AI Act to manage risks – but is now recalibrating to avoid stifling innovation. Brussels has signaled a willingness to adjust regulations and offer more support for startups and data-sharing, as seen in the recent AI Innovation Package and AI Continent Action Plan focused on funding, public compute infrastructure, and harmonised data spaces for AI development. Europe is also leveraging partnerships to amplify its efforts: the EU-Japan Digital Partnership, for example, promotes joint work on human-centric AI and digital standards, and European governments are courting foreign chipmakers (from Taiwan, South Korea, and the U.S.) to build plants in Europe. Still, Europe faces a perception gap – it is not yet seen as a primary AI leader, and its companies lack the scale of American or Chinese behemoths. The region’s challenge and priority is to unite its market, invest at scale, and collaborate globally so that it can ride the AI wave rather than watch from behind.

Lessons from the Internet Era (2000–2020)

The past two decades of the internet revolution offer critical lessons for policymakers to ensure Europe doesn’t miss the AI era:

• Scale and First-Mover Advantage: The U.S. and China attained digital dominance by scaling innovations rapidly and cultivating giant tech firms early. American companies like Google, Apple, and Facebook leveraged a large unified market and abundant venture capital to achieve global scale, establishing de facto standards and platforms. China, meanwhile, combined a huge domestic user base with protective policies (blocking Western rivals) and state support, enabling homegrown titans (Alibaba, Tencent, Baidu) to flourish behind the “Great Firewall.” By the time Europe’s alternatives emerged, network effects and capital scale firmly favored the U.S. and China. Lesson: In AI, being early and bold in scaling up (from funding startups to deploying AI at population scale) is crucial – latecomers face entrenched incumbents.
• Unified Vision vs. Fragmentation: A fragmented approach held Europe back in the internet age. The EU had strong technical talent and innovators (indeed, the web was invented at CERN in Europe), but European firms struggled to unite the continent’s diverse markets. In the 2000s, multiple European social media or e-commerce platforms remained country-bound, while U.S. platforms expanded continent-wide. Policy fragmentation and varying regulations across EU states hindered the emergence of an EU-wide tech giant, whereas the U.S. and China each operated at the continental scale. Lesson: European countries must cooperate on AI – sharing data, aligning standards, and pooling research – to create a market big enough for globally competitive AI firms. Pan-European initiatives (like joint research centers or cross-border testbeds) can prevent the “silo effect” that previously kept innovation local.
• Regulation Timing and Innovation Climate: Europe often responded to tech revolutions with regulation after the fact – for example, enforcing privacy (GDPR) or competition rules once U.S. firms were already dominant. While these regulations reflected core European values, they had the unintended effect of burdening European startups (with compliance costs) more than the well-resourced incumbents. By contrast, the U.S. maintained a lighter regulatory touch in the formative years of internet and social media growth, and China aggressively subsidized tech expansion, prioritizing growth first. Lesson: As AI matures, Europe must balance its ethical/regulatory leadership and foster an innovation-friendly environment. Proactive measures like sandboxes (for testing AI under flexible rules), streamlined approvals, and substantial R&D incentives are needed so European AI firms can experiment and scale without undue hurdles. Smart regulation that protects rights and promotes innovation will distinguish those who lead from those who lag.

In summary, the internet era taught that waiting too long to scale inventions or to create enabling policy can leave a region dependent on others’ technology. Europe’s goal now is to apply these lessons – by being bold, cohesive, and innovation-forward – so that the AI revolution will be “made in Europe” as much as in Silicon Valley or China.

Seizing the AI Opportunity: Strategies for Germany, France, the UK and beyond

Europe’s largest economies each have unique strengths that, if leveraged with a strategic vision and global partnerships, can position them at the forefront of AI. Below are targeted recommendations:

1. Germany: Leverage Industrial Might with Tech Partnerships. Germany’s world-class manufacturing base and Mittelstand companies are ideal for AI-driven industrial transformation. To seize this opportunity, Germany should double down on initiatives like Industry 4.0, integrating AI for more intelligent factories, precision engineering, and supply chain optimization. This means investing in industrial AI research centers and testbeds where manufacturers and tech firms co-develop solutions (e.g. machine vision for quality control, predictive maintenance for machinery). Given geopolitical shifts, Germany can bolster its tech resilience by partnering with like-minded Asian economies that excel in hardware – for example, South Korea and JapanAdditionally, Berlin must ensure that mid-sized German firms have access to the necessary data and cloud infrastructure – perhaps via a European AI cloud – so they can implement AI without ceding control to foreign platforms. Germany can remain an export powerhouse in the AI age by focusing on its strength in trusted high-quality manufacturing and teaming up with internationally. Notably, this aligns with the country’s push for “digital sovereignty” – succeeding in AI as part of a stronger EU tech ecosystem rather than depending on U.S. or Chinese tech.
2. France: Build on Research & Attract Global Capital. France boasts a rich tradition in mathematics and AI research (Paris is a hub for AI talent) and has a dynamic startup scene emerging (e.g. companies like Mistral AI). To capitalize on this, France should act as Europe’s AI innovation engine – turning its research leadership into scalable enterprises. A key step is enhancing the commercialization of AI research: expand public-private incubators that connect university labs (like INRIA or CNRS) with industry and venture investors, so that more “made in France” algorithms and models can become products. President Macron’s recent strategy underscores this approach: France is attracting massive global investment (over €100 billion pledged) into its AI and semiconductor sectors. The government should ensure these funds (from the UAE, North America, and elsewhere) are channeled into building cutting-edge data centers, cloud infrastructure, and training programs on French soil. Global partnerships are central here – France and Europe plan to co-invest with American and Asian firms in new chip fabs and AI hubs, which is wise. For example, partnering with Taiwan’s TSMC or South Korea’s Samsung to establish semiconductor facilities in France would secure supply chains and create high-tech jobs. Simultaneously, France can leverage its leadership in AI governance (it co-founded the Global Partnership on AI) to shape international norms in a way that favors its strengths in trustworthy AI. A strategic niche for France is also AI in aerospace and defense – building on firms like Airbus and Thales – where it can collaborate with allies (such as Japan or India, via established strategic partnerships) on next-gen AI for satellites, drones, and more. Finally, reducing bureaucratic hurdles for tech deployment, as promised at the Paris AI summit, will accelerate progress. By marrying its intellectual capital with global financial and industrial partnerships, France can emerge as Europe’s AI powerhouse and ensure homegrown innovations thrive rather than being snapped up by foreign big tech.
3. United Kingdom: Become the Global AI Hub and Bridge. The UK aims to position itself as a global AI hub, leveraging its agile regulatory environment and strong international networks. The government’s new AI Opportunities Action Plan explicitly seeks to make the UK an “AI maker, not an AI taker” – in other words, a leading creator of AI technologies. To achieve this, Britain should invest in AI clusters and infrastructure that attract both domestic entrepreneurs and overseas AI labs. Plans are underway to create regional data center hubs with fast-track planning and ample power supply; these must be executed swiftly so AI companies find the UK an easy place to scale (e.g. a company like DeepMind grew out of London – the UK should cultivate the next DeepMind locally rather than see it acquired abroad). The UK’s strength in finance and services also means it can become a leader in applied AI for fintech, lawtech, and healthcare (leveraging the NHS’s vast datasets under proper ethics). Critically, the UK should use its convening power on the global stage: it has already hosted dialogues on AI safety at Bletchley Park and can serve as a bridge between the US, Europe, and Asia on AI collaboration. Strengthening ties with the US is natural (the two countries are exploring a bilateral technology partnership), but equally the UK can deepen cooperation with Asian tech leaders. For instance, partnering with India on AI skill development would tap into India’s huge IT talent pool, while joint research with Japan or Singapore on AI governance and security can bolster the UK’s influence in setting global standards. Moreover, by adjusting immigration rules to attract top AI experts worldwide, the UK can become the destination of choice for AI talent (it already draws many researchers from Europe and Asia due to English language and strong universities). In summary, the UK’s opportunity is to be the innovation testbed and diplomatic broker in the AI era – fostering an environment where AI startups thrive under light but sound regulation, and where East-West collaborations find common ground. This will not only boost the UK’s economy but also ensure it helps shape AI’s global trajectory in line with democratic values.

Conclusion – Across all these recommendations, a unifying theme emerges: proactive engagement. Europe’s key nations must act decisively – investing in strategic infrastructure, nurturing talent, and partnering beyond their borders – to ride the AI wave. The next tech era will be defined by those who build and scale AI solutions and set the rules for their use. With foresight and collaboration, Europe (from London and Paris to Berlin and Brussels) can be among those leaders, ensuring the AI revolution delivers prosperity and innovation on its own terms, rather than happening to it. The window of opportunity is open now, and the choices made in the next few years will determine whether Europe leads or lags in the defining technology of the 21st century.