AI strategies in a global comparison: A comparison (USA vs. EU vs. Germany vs. Asia vs. China)

$400 billion bet: Why the US is investing in AI out of sheer panic – and not strategy

The five largest economic regions have dramatically different philosophies on whether or not to develop an AI strategy. These differences reveal deep contradictions between technological ambition, economic reality, and strategic necessity.

USA: “Defining the playing field” (Deregulation instead of strategy)

Regional perception

For the US, an isolated “AI strategy” is not the core issue. Instead, the Trump administration is pursuing a radical deregulation approach that positions AI as a strategic weapon against China. The US is relying on three pillars: accelerating innovation, expanding infrastructure, and establishing a global leadership position.

The paradox

With $400 billion in planned AI investments by 2025 from Amazon, Meta, Microsoft, and Google, AI has de facto become a matter of national interest. However, at the corporate level, this isn't driven by consultative AI strategy processes, but by the necessity of capital: Deutsche Bank warned as early as 2024 that without massive AI investments, the US would already be in recession. This isn't a choice – it's economic survival.

The US exemplifies the "hype without added value" error. 95 percent of American companies have yet to achieve a measurable return on their generative AI investments. At the same time, OpenAI CEO Sam Altman warned of an AI bubble. The system works nonetheless because it relies on infrastructure dominance, not on rational ROI.

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EU: “AI-First with a demand for control” (strategy instead of room for maneuver)

Regional perception

The EU is taking an anti-hyper stance – while simultaneously developing one of the most comprehensive AI strategies ever. The “Apply AI Strategy” of October 2025 combines an “AI-first” approach with “Buy European” principles.

The fundamental conflict

The EU recognizes that AI is a cross-cutting technology, but integrates it through strategic management: “The introduction of AI in ten key sectors – from health and mobility to defense – will be specifically promoted.” One billion euros in public funds will be used to establish “AI Experience Centres” to support small and medium-sized enterprises (SMEs) in their implementation.

The EU is making the opposite mistake to the US: it's over-bureaucratizing. Instead of "less is more," the motto is "strategy upon strategy upon regulation." The AI ​​Act, national regulations, the Apply AI Strategy, the AI ​​in Science Strategy – it's all orchestrated to the point of paralysis. The compliance burden is particularly enormous for SMEs.

Bitkom warns: Without “more innovation-friendly regulation, AI specialists and competitive electricity prices”, the EU will lose the race.

Germany: “Paralysis through over-analysis” (strategy, but without clarity)

Regional perception

Germany is a country of compromise – and thus a country of indecisiveness. Officially, Germany enshrined its “German AI Strategy” in the 2025 coalition agreement and positioned AI as a core project. In practice, however, AI remains a puzzle for German SMEs, offering no clear answers.

The data situation is devastating.

• 36 percent of companies use AI (2024: 20%), but only 21 percent have a real AI strategy.
• Among SMEs with 20-49 employees, the rate of AI strategy is only 9 percent.
• 68 percent of SMEs do not have a detailed AI roadmap
• 53 percent see legal hurdles as the biggest obstacle, 82 percent report gaps in expertise.

The critical correspondence

•  Tech obsession without a business focus: Technology is sold as the solution, not the business problems. “We need an AI strategy” instead of “How do we optimize our process cost ratio by 12%?”
• Fragmented strategies instead of orchestration: Everyone talks about AI strategy, parallel RPA, data strategy, edge computing – but rarely integrated. This is precisely the “sub-strategy silo error” from the original.
• Paralysis due to uncertainty: The combination of the EU AI Act, national regulatory ideas, and data protection hypervigilance means that while 47 percent of companies are planning or discussing, 43 percent have ZERO concrete strategy.

The 2025 coalition agreement signals: Now things will be “innovation-friendly.” But the reality for SMEs is still a regulatory sandbox – experimenting under observation instead of operating in the market.

Asia (Japan & South Korea): “National Mobilization without Hypocrisy”

Regional perception

Asia is radically different: Here, AI strategies are not marketing tools, but national mobilization plans.

• South Korea has implemented the “M.AX strategy” (Manufacturing Artificial Intelligence Transformation) from the top down – over 1,000 companies, research institutions, and the government are working together towards this goal: to become a top-3 AI nation. This is not a strategy in the European sense (regulation + guidelines), but rather a coordinated invasion of new markets, with semiconductors, renewable energies, and defense as application domains.
• Japan, on the other hand, has taken a pragmatic middle ground: an AI strategy since 2017, AI guidelines for companies in 2024, and an AI law in 2025 – but stricter than the US, more flexible than the EU. Japan is leveraging its strengths in materials science and mechanical engineering for specialized AI applications.

Asia implicitly contradicts BOTH positions:

• Against “just business value”: Without national coordination (South Korea) or specialized strengths (Japan), individual companies cannot compete against China and the USA.
• Against “overregulation”: South Korea and Japan regulate in a targeted, not fragmented, manner. M.AX has clear sectors and KPIs, not endless compliance labyrinths.

China: “Total integration instead of strategic thinking” (AI as an operating system, not technology)

Regional perception

China has transcended strategic thinking. With the “AI+ Action” (2025), AI is not treated as a specialized technology, but as a new operating system for the economy.

The 14-point plan aims to

• By 2027: Deep AI integration in 6 core areas (research, industry, consumption, public sector), over 70% adoption of AI agents
• By 2030: AI as a key economic driver
• By 2035: Complete “Intelligent Economy & Society”

87 percent of Chinese companies plan to increase their investment in AI by 2025. This is not planning – this is economic warfare mobilization.

The critical correspondence

• AI as a technology is obsolete. China is not implementing AI – it is transforming towards AI. This is not “a strategy”, but a systemic transformation.
• “Less is more” doesn't work in global competition. China doesn't invest rationally based on ROI – China invests for its very survival. Without this aggressiveness, China will lose the race against the US and Western regulatory powers.
• Regulation is happening every second. China has published 30 national AI standards, with another 84 in development – ​​not as a hurdle, but as a control and standardization tool for scaling and standardization.

The dilemma

An isolated “AI strategy” doesn't work for China either – because China has long since declared it state doctrine.

Global AI strategies compared: Who focuses on transformation, who on regulation?

In the US, artificial intelligence is primarily viewed as infrastructure rather than a standalone strategy. Despite investments of around $400 billion, it mainly serves economic survival, with 95 percent of projects failing to generate a return – driven by systemic pressures. The European Union, on the other hand, pursues an AI-first strategy with a clear governance framework, coupled with public investments of one billion euros. However, overregulation and a shortage of skilled workers stifle innovation. Germany suffers from a strategic paralysis caused by excessive analysis: While 36 percent of companies use AI, only 21 percent do so with a clear strategy. The result is fragmented sub-strategies and a lack of orchestration. In Asia, countries like South Korea and Japan are mobilizing AI nationally and focusing on specialized niches – South Korea with a coordinated offensive, Japan with focused excellence – but are heavily dependent on technologies from the US and China. China, in turn, understands AI not merely as a strategy, but as a comprehensive transformation and is investing massively, including through a 14-point master plan. For 2025, 87 percent of companies there plan to increase spending, but face geopolitical tensions and technological dependencies in semiconductors.

Regional tensions – but only for Germany

“Added value instead of technology”, “Orchestration instead of individual tools”, “Strategy instead of sub-strategies” is right for Germany. But:

1. For the US and China: Not relevant. There, AI is no longer a “strategic option”—it's an economic necessity. “Less is more” works when you're not engaged in a global technology war.
2. For the EU: Paradoxically, the EU focuses too much on strategy (regulation) while creating too little infrastructure. The “Apply AI Strategy” is well-designed (sectoral, not technology-driven), but internal EU fragmentation (national AI Act, data localization, compliance labyrinths) undermines it.
3. For Asia: National coordination (South Korea) + specialized excellence (Japan) functions as a Third Way: strategic focus without over-regulation, but with state coordination.
4. For China: The AI+ initiative is not a strategy in the sense of Western management literature – it is a systemic transformation. China is already applying the original argument (business value before technology), but at a macro level.

Conclusion for Germany (and Europe): The risk of mediocrity

Germany's critical stance is methodologically correct:

• Don't hit everything with the AI ​​hammer.
• Added value before technology
• Orchestration instead of isolation

But regionally, this is a luxury position.

Germany and Europe can only afford “less is more” if they:

1. Building infrastructure sovereignty (AI gigafactories, computing capacity) – currently lagging behind
2. Stabilizing the skilled worker pipeline – 82% of SMEs complain about skills gaps
3. Simplify regulation from complexity to pragmatic clarity – not ADD strategies.
4. Operationalize orchestration – don't just preach.

The dilemma

While Germany is still debating whether an AI strategy makes sense, China (70% adoption by 2027), the USA ($400 billion), and South Korea (M.AX mobilization) are accelerating their efforts. The question is no longer "do we need an AI strategy?" but "how quickly can we set the right priorities?"

Sometimes less is more. But sometimes “too late” is the most expensive of all strategies.

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The dangerous luxury of indecisiveness: Why Germany's caution is leading Europe into irrelevance

The question of whether a standalone AI strategy is necessary has evolved over the past two years from an academic debate to an existential challenge for nation-states. While management consultants and economic analysts are still debating whether companies actually need isolated AI strategies or whether integration into existing business processes would be more sensible, the major economic regions have long since taken action. This action reveals a fundamental divide in the global economic order: On the one hand, there are those nations that treat AI as an economic necessity and are mobilizing massive resources accordingly. On the other hand, there are those that remain stuck in strategy papers, debating the optimal governance structure, while technological sovereignty slips through their fingers.

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The American imperative: Dominance through deregulation and capital

The United States has chosen a path that appears paradoxical at first glance. The Trump administration is pursuing a radical deregulation approach and explicitly positioning AI as a strategic weapon in competition with China. In July 2025, the White House published the comprehensive action plan for American AI leadership, which includes over ninety concrete measures. These are structured around three pillars: accelerating innovation by removing regulatory hurdles, massive infrastructure expansion, and international diplomacy to establish American standards. It becomes clear that the US does not treat AI as an isolated technological issue, but rather as an integral component of national security and economic dominance.

The scale of this strategy only becomes apparent when considering the specific investment sums. The four major technology companies—Amazon, Meta, Microsoft, and Google—have announced capital expenditures of approximately $400 billion for 2025, the majority of which will flow into AI infrastructure. These investments are not driven by free will or entrepreneurial vision, but by the necessity of economic survival. An analysis by Deutsche Bank from the fall of 2024 revealed a startling finding: without these massive AI investments, the United States would already be in recession or on the verge of one. AI machines are literally saving the American economy, as Deutsche Bank's Global Head of FX Research put it. Between the fourth quarter of 2024 and mid-2025, the contribution of data center construction to the American gross domestic product even exceeded the contribution of private consumption.

The billion-dollar risk: Infrastructure development without a guaranteed return on investment

This dependence, however, also reveals the fundamental weakness of the American approach. Ninety-five percent of American companies have yet to achieve a measurable return on their investments in generative AI. A study by the renowned Massachusetts Institute of Technology from the summer of 2025 documented that ninety-five percent of all generative AI pilot projects in companies fail and generate no return on investment. Even OpenAI CEO Sam Altman issued a stark warning in August 2025 about an AI bubble, drawing explicit parallels to the dot-com crisis of the late 1990s. Altman stated that during bubbles, intelligent people tend to become excessively euphoric about a kernel of truth. His assessment was unequivocal: Yes, we are in a phase where investors as a whole are overexcited about AI.

The US thus perfectly exemplifies the very mistake that critics of the AI ​​strategy proliferation denounce: hype without a consistent focus on measurable added value. The system nevertheless functions because it relies on infrastructure dominance rather than a rational return on investment. The American strategy is based on the assumption that whoever controls the largest AI ecosystem will set global standards and gain comprehensive economic and military advantages. This is no longer a business decision, but rather an economic survival strategy at the nation-state level.

Fortress Europe: Security and regulation as the core of the brand

The European Union is deliberately positioning itself as a counterpoint to this deregulated approach. On October 8, 2025, the European Commission published its Apply AI Strategy, which combines an AI-first approach with Buy European principles. The strategy aims to systematically introduce AI into ten key sectors, including healthcare, mobility, manufacturing, energy, and defense. With one billion euros from public funding programs such as Horizon Europe, Digital Europe, EU4Health, and Creative Europe, AI experience centers will be established to support small and medium-sized enterprises (SMEs) in particular in their use of AI. The existing European Digital Innovation Hubs will be transformed into AI Experience Centers, complemented by AI factories, testing and experimentation environments, and regulatory sandboxes.

The European strategy thus recognizes that AI is a cross-cutting technology, but integrates it through extensive strategic management and regulation. This marks the fundamental difference from the American approach: While the US prioritizes maximum freedom of innovation, Europe has chosen the path of orchestrated development under strict legal frameworks. The AI ​​Act, which came into force in August 2024, establishes a risk-based regulatory system, considered the world's first comprehensive AI law. The regulation provides for staggered implementation dates, with prohibitions on certain AI practices already in effect since February 2025 and the governance and sanctions provisions fully applicable since August 2025.

The digital association Bitkom welcomed the Apply AI Strategy as an important shift in awareness regarding artificial intelligence. The commitment to an AI-first principle, in which AI will become an integral part of economic value creation, public administration, and research, represents a significant step toward strengthening European competitiveness. At the same time, however, the association cautioned that programs and strategies alone are insufficient. Other countries, especially the USA and China, have planned AI infrastructure projects on a significantly larger scale, amounting to €500 billion. Europe can only achieve its ambitious goals if public investment is complemented by private capital. This requires innovation-friendly regulations as well as excellent business conditions, from a skilled AI workforce to competitive electricity prices.

Germany's paradox: Ambitious goals meet hesitant implementation

This reference to location-specific conditions reveals the central contradiction of the European strategy: the EU is over-strategizing. Instead of the principle of "less is more," the motto is "strategy upon strategy upon regulation." The AI ​​Act, national regulations, the Apply AI Strategy, the AI ​​in Science Strategy, the various national implementing laws for the AI ​​Act—all of this is orchestrated to the point of paralysis. For small and medium-sized enterprises (SMEs), the compliance burden represents an enormous hurdle. Only thirteen and a half percent of European companies and twelve and a half percent of SMEs currently use AI technologies, as the European Commission determined in spring 2025.

Germany occupies a paradoxical position within Europe. The country of moderation has thus become a land of indecisiveness. In April 2025, the new coalition agreement enshrined AI as a core project of the German government and formulated the goal of making Germany the leading AI nation in Europe. The coalition plans massive investments in digital infrastructure and the expansion of AI capacities. Key measures include the establishment of a national AI gigafactory with a pool of at least 100,000 graphics processors for research institutions and universities, the creation of AI real-world laboratories for testing innovative applications under real-world conditions, and an innovation-friendly implementation of the EU AI Act to reduce the burden on businesses.

In practice, however, a huge gap exists between political aspirations and operational reality. In September 2025, the digital association Bitkom published a representative survey of 604 companies in Germany with 20 or more employees. The results show a significant increase: 36 percent of companies now use AI, almost twice as many as a year earlier, when the figure was 20 percent. Another 47 percent are currently planning or discussing the use of AI. In contrast, only 17 percent now state that AI is not relevant to them, compared to 41 percent the previous year.

Reality check for SMEs: Skilled worker shortage and legal uncertainty

These positive figures, however, should not obscure the fact that only 21 percent of companies have a genuine AI strategy. A comprehensive AI study for SMEs from 2025 revealed the full extent of the problem: 68 percent of the surveyed companies lack a well-developed AI roadmap. 81 percent do not systematically measure the return on investment of their AI initiatives. Only 19 percent have established a dedicated AI manager or AI team. 54 percent don't even know which AI use cases are relevant to their business.

The skills gap represents the biggest obstacle. Eighty-two percent of companies report significant skills gaps in AI. A study by the Stifterverband and McKinsey from January 2025 found that seventy-nine percent of the companies surveyed stated they lacked the necessary AI skills. Particularly alarming: Eighty-two percent of respondents criticize German universities for poorly preparing students for the new, AI-driven world of work. The gap between academic training and the practical demands of the economy appears especially large in the field of AI.

Legal uncertainties add to the challenge. Fifty-three percent of companies see legal hurdles as the biggest obstacle to AI investments. The combination of the EU AI Act, national regulatory proposals, and data privacy oversight leads to forty-four percent of companies citing regulatory uncertainty as a barrier to innovation. Forty-three percent have no concrete AI strategy whatsoever, while another forty-seven percent plan and discuss but do not take action.

Germany is thus suffering from both of the flaws that critics of an isolated AI strategy denounce: On the one hand, there is a prevailing tech obsession without a business focus. Technology is being sold as the solution, not the concrete business problems. Companies are asking, "We need an AI strategy," instead of asking, "How can we optimize our process cost ratio by twelve percent through targeted technological interventions?" On the other hand, a fragmentation into unconnected sub-strategies dominates: AI strategy, RPA strategy, data strategy, and edge computing strategy exist side by side, but rarely in an integrated manner. This corresponds exactly to the sub-strategy silo error that management experts warn against.

The combination of poor orchestration and regulatory overload creates paralysis through uncertainty. While the 2025 coalition agreement signals a more innovation-friendly course, the reality for small and medium-sized enterprises (SMEs) remains characterized by regulatory sandboxes: experimenting under observation instead of operating in the market. While policymakers are still debating the optimal design of the national market surveillance authority for the AI ​​Act and discussing whether it should be organized at the federal or state level, other nations are investing hundreds of billions in actual infrastructure.

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The third way: Asia's pragmatic industrial mobilization

The Asian economies of Japan and South Korea are pursuing fundamentally different approaches. In September 2024, South Korea adopted the M.AX strategy, short for Manufacturing Artificial Intelligence Transformation. This is not a strategy in the European sense of regulations and guidelines, but rather a national mobilization plan involving over a thousand companies, research institutions, and government agencies. The goal is unambiguous: South Korea aims to become one of the world's three leading AI nations.

In August 2025, the South Korean government made AI investment its top political priority. Over the next five years, thirty AI projects are to be implemented through a public-private investment fund worth seventy-six billion US dollars. The government aims to foster startups in the field of AI services and solutions and cultivate five global AI unicorns. By 2028, the world's largest AI data center, with a capacity of three gigawatts, is to be built, financed with up to thirty-five billion US dollars. The goals are quantified: by 2030, an AI adoption rate of seventy percent in industry and ninety-five percent in the public sector is to be achieved.

The M.AX strategy not only addresses the next generation of semiconductors from companies like Samsung and SK Hynix, but also encompasses the promotion of renewable energies, the development of new medicines, defense, and other heavy industry products. There is talk of a national AI database, although no further details are yet available. The picture is clear, however: South Korea is pulling together, and competitors are cooperating, at least in part, to help shape the AI ​​boom. This is a coordinated invasion of new markets, not a regulatory declaration of intent.

Japan is taking a more pragmatic middle ground. The country developed an AI technology strategy as early as 2017 and formulated the AI ​​Strategy 2022 in 2022, which aims to leverage Japan's strengths in materials, pharmaceuticals, and mechanical engineering for AI applications. AI guidelines for companies followed in April 2024. In May 2025, the Japanese parliament passed an AI law requiring companies to use AI responsibly and cooperate with the government. The rules are stricter than those in the US, but allow more flexibility than in the EU.

The Digital Infrastructure Plan 2030, published in June 2025, sets out specific funding priorities: AI data centers, submarine cables, pure optical networks, post-5G telecommunications infrastructure, and quantum cryptography communication. The plan is complemented by a strategy for global expansion. Japanese companies are to lay more than 35 percent of the total length of new submarine cables worldwide between 2026 and 2030. They are also expected to secure more than one-fifth of the global market for data centers by 2030.

Japan and South Korea thus implicitly contradict both positions in the European debate. Against the argument that only business value counts, they advocate national coordination. Without state orchestration, individual companies could not compete against China and the USA. Against overregulation, they advocate targeted management instead of fragmented compliance labyrinths. M.AX has clearly defined sectors and measurable performance indicators, not endless regulatory processes. South Korea and Japan leverage their respective strengths in specialized niches: South Korea the semiconductor industry and heavy industry, Japan materials science and precision engineering.

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China's holistic approach: AI as a systemic operating system

China, however, has transcended strategic thinking. In September 2025, the People's Republic officially launched its AI Plus initiative, a fourteen-point master plan with the ambitious goal of deeply integrating AI into every aspect of the economy, society, and government. This is not a strategy paper in the Western sense, but a concrete roadmap for systemic transformation. The plan is structured around six key areas of action, supported by eight measures designed to strengthen foundational capabilities.

The goals are precisely defined in terms of time: By 2027, deep AI integration is to be achieved in six core areas: research, industry, consumption, general prosperity, administration, and global cooperation. The penetration rate of AI agents and smart devices is to exceed 70 percent. By 2030, AI is to become the central economic driver, with a penetration rate of over 90 percent. The smart economy will then become the main driver of growth. By 2035, the complete transition to a smart economy and society is targeted. AI will then be a cornerstone of national modernization.

A survey conducted by the global consulting firm Accenture in February 2025 documented the pace of China's transformation: Eighty-seven percent of the Chinese companies surveyed plan to increase their investments in AI in 2025. Fifty-eight percent of the executives surveyed in China believe that their companies' AI development is progressing faster than originally anticipated. Fifty-eight percent expect their generative AI solutions to be widely deployed within their companies by 2025, an increase of thirty-two percentage points compared to 2024.

China treats AI not as a technology, but as a new operating system for the economy. Current investments by Chinese companies in generative AI are primarily focused on core technology infrastructure and data, such as AI platforms, cloud and data management, and talent and skills development. The three main areas for the planned adoption of generative AI by 2025 are information technology, engineering and manufacturing, and research and development.

China has also published thirty national AI standards, with another eighty-four in development. This serves not as a hurdle, but as a control and standardization tool for scaling. An isolated AI strategy is also ineffective for China, as the country has long since enshrined it as state doctrine. In July 2025, the Chinese government proposed establishing a global organization for cooperation in artificial intelligence. It emphasized the importance of strengthening coordination between countries to create a globally recognized framework for the development and safety of AI. China aims to play a leading role in the global discussion surrounding this technology.

Strategic Dissonance: Why Western Management Theories Fail Globally

This regional comparison reveals a fundamental tension. The initial argument—that companies should focus on added value rather than technology, orchestrate rather than deploy individual tools, and pursue integrated strategies instead of fragmented sub-strategies—is methodologically sound and highly relevant for Germany. Germany should indeed avoid a blanket approach to AI; it should prioritize added value over technology and practice orchestration rather than isolation.

For the US and China, however, this recommendation is irrelevant. There, AI is no longer a strategic option, but an economic necessity. Less is more doesn't work when you're engaged in a global technology war. The US isn't investing four hundred billion dollars annually in AI infrastructure based on rational ROI calculations, but because without these investments, the economy would slide into recession. China isn't investing according to business metrics, but out of sheer necessity. Without this aggressive approach, China would lose the race against the US and regulatory Western powers.

A paradox arises for the European Union: the EU is focusing too much on strategy in the form of regulation while creating too little infrastructure. The Apply AI Strategy is conceptually sound, being sector-based rather than technology-driven. However, internal EU fragmentation undermines it: national AI Acts, data localization, and the compliance labyrinths of various member states. Each member state must designate or establish three types of authorities: a national competent authority as a central point of contact, a notifying authority for the accreditation of conformity assessment bodies, and a market surveillance authority for the practical control of AI products. In Germany, the Federal Office for Information Security (BSI) and the Federal Network Agency (BNetzA) are expected to assume these roles. The question of whether oversight should be organized at the federal or state level remains unresolved.

For Asia, national coordination functions as a third way: strategic focus without overregulation, but with state coordination. South Korea's M.AX is not a European regulatory strategy, but rather coordinated economic mobilization. Japan's pragmatic approach combines specialized excellence with targeted government support, without a stifling compliance regime.

The final dilemma: loss of sovereignty through perfectionism

Germany and Europe are thus facing a fundamental dilemma. The recommendation to focus on business value, practice orchestration, and pursue integrated rather than fragmented strategies remains normatively sound. However, Germany and Europe can only afford to adopt a "less is more" approach under several conditions: firstly, the development of infrastructure sovereignty through AI gigafactories and sufficient computing capacity. Germany currently lags behind in this area. In November 2025, the installed capacity of all German data centers was 2,980 megawatts. AI data centers accounted for fifteen percent of this, or 530 megawatts. This is projected to quadruple to 2,020 megawatts by 2030. By comparison, the USA and China are planning AI infrastructure projects on the order of 500 billion euros.

Secondly, Germany needs a stable pipeline of skilled workers. Eighty-two percent of German SMEs complain about skills gaps, and only twenty-one percent have implemented structured AI training. Seventy-three percent do not systematically train their employees in AI topics. Eighty-nine percent have difficulty recruiting AI talent. This is not a temporary problem, but a structural threat to competitiveness.

Thirdly, regulation must be simplified from complexity to pragmatic clarity, not by adding further layers of strategy. In October 2025, the Green Party parliamentary group in the Bundestag urged that the national implementing legislation for the European AI Regulation be introduced to the Bundestag before the end of 2025. The aim was to establish clear responsibilities and ensure sufficient resources. The planned AI market surveillance chamber must be organized in such a way that it can operate truly independently. The feasibility of consolidating oversight of various EU digital laws in a single coordination body should be examined. All of these are necessary steps. However, they are being taken while other nations have long since established facts on the ground.

Fourth, orchestration must be operationalized, not just preached. Most German companies talk about an AI strategy, but in parallel, they have unconnected RPA, data, edge computing, and other strategies. This fragmentation prevents synergistic effects and leads to duplicate structures and inefficient resource allocation.

The central dilemma is this: While Germany is still debating whether an AI strategy makes sense and how it should be optimally designed, China is accelerating its efforts with its target of 70 percent AI adoption by 2027, the US with its 400 billion dollar annual investment, and South Korea with its M.AX mobilization. The question is no longer whether we need an AI strategy, but how quickly we can set the right priorities.

The original argument remains true, but as a normative ideal, not as a practical guide for the present. Less is sometimes indeed more. But sometimes being too late is the most expensive of all strategies. Germany and Europe are not risking the loss of individual markets or technological fields. They are risking sinking into economic insignificance in the crucial decade of the twenty-first century, while others define the standards, infrastructures, and thus the power structures of the coming decades.

The critical difference lies not in whether or not one should have an AI strategy. It lies in the speed, consistency, and resource mobilization of its implementation. Due to differing system logics, the USA and China have both recognized that AI is no longer a management issue, but a matter of survival. Europe and Germany still treat AI as just one optimization project among many. This misjudgment could prove to be a historic mistake, one that will be irreversible once technological sovereignty has irrevocably shifted to other economic regions.

Konrad Wolfenstein

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