Simply skipping ahead? Europe's second chance lies not in copying, but in intelligently skipping missed developmental stages

Missed out on software, gained the future? Nvidia's CEO explains Europe's "unfair" advantage in the next industrial revolution

Nvidia CEO provokes business leaders: "You've missed the boat on software" – and offers the ingenious solution

When, at the World Economic Forum in Davos, the CEO of an American technology company offers Europe strategic advice that previously regularly caused irritation in client meetings, it's worth taking a sober look at what Jensen Huang of NVIDIA said to the world's economic leaders in January 2026: Stop chasing Silicon Valley. You've missed the software era. Just skip it. This exhortation is far more than a polite encouragement to an uncertain continent. It's a precise diagnosis of structural competitive dynamics and, at the same time, the outline of a strategy that could combine Europe's industrial DNA with the possibilities of physical artificial intelligence.

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• Technological leap through leapfrogging: Europe's and Germany's chance for technological transformation despite China's dominance

Why copying market leaders is structurally doomed to failure

The central insight from strategic competitive research is strikingly simple: anyone who chases a market leader and imitates its moves systematically widens the gap to the top. The reason lies in the asymmetrical distribution of speed and resources. A market leader isn't at the top by chance, but because it is faster in implementation, has established distribution channels, leverages economies of scale, and sets the standards by which the market operates. Every attempt to catch up through mere imitation fails due to a simple matter of timing: while the pursuer is still replicating yesterday's steps, the market leader has already made the next three moves.

This dynamic was exemplified in the automotive industry. Six years before Huang's appearance at Davos, a project for a major German automaker revealed the structural inefficiency of imitating Tesla's innovations. As a pioneer, Tesla had not only established a technological lead in battery technology and software integration, but, more importantly, had developed an organizational speed that traditional manufacturers with their established structures could not match. While German engineers attempted to replicate Tesla's over-the-air updates, Tesla had long since further developed autonomous driving functions and revolutionized its production processes with the Gigacasting method. The delay was not due to a lack of competence, but rather a systematic speed disadvantage: the market leader set the pace, and the imitator reacted.

The empirical data clearly confirms this observation. Tesla achieved a profit margin of twelve percent in 2021, while European manufacturers struggled with chip shortages and production bottlenecks. BMW and Mercedes achieved similar margins, but only through a drastic strategy: they concentrated their scarce chips on high-margin premium models and deliberately avoided volume production. This was not a strategy born of strength, but a necessary measure. The shift is now even more pronounced: in November 2025, the Tesla Model 3 and Model Y continued to lead European electric car sales, but competitive pressure from the Renault 5, Skoda Elroq, and VW ID.3 was increasing. Europe was catching up, not by copying, but by launching its own model offensives in segments that Tesla had neglected.

The lesson from these developments is not that innovation is impossible, but that imitation strategies waste time and resources that are then lacking for differentiated positioning. Companies like Zara in fashion and Amazon in logistics demonstrate the opposite: they set standards through radical process innovation. Zara managed to bring new designs to stores within two weeks, thereby setting trends instead of following them. Amazon built a fully automated delivery system based on speed and algorithms, not on replicating traditional retail models. In both cases, the strategy was not imitation, but structural differentiation.

The paradigmatic shift from programmed software to trained intelligence

Jensen Huang's central thesis at the World Economic Forum was precisely formulated: In the age of AI, no one writes software anymore; AI is trained, not programmed. This statement marks a fundamental paradigm shift in how technological systems are created. In the software era dominated by Silicon Valley, programming was at the heart of value creation. Engineers wrote line after line of code in languages ​​like C, Python, or Java to implement precisely defined algorithms. These systems were deterministic: For every input, there was a predictable output. Whoever had the best programmers could build the best software products. Europe had structurally lost out in this competition because the US had a larger number of highly skilled software developers, a more aggressive venture capital culture, and an ecosystem that rewarded scaling.

With the widespread adoption of AI systems, the logic changes completely. Modern AI models are no longer programmed, but trained with data. A Large Language Model like GPT is not created by writing rules, but by feeding neural networks billions of text examples, from which the system independently recognizes patterns. Huang illustrated this at London Tech Week in June 2025 with a compelling analogy: You program AI like you program a human. You say: You're a great poet, you know Shakespeare, write me a poem about this keynote. The AI ​​generates an initial version. You give feedback: I think you can do better. The AI ​​reflects and delivers an improved version. This interaction is fundamentally different from writing code.

The consequences of this shift are far-reaching. Programming as an activity is not losing its importance, but its role is changing. Huang stated at the World Government Summit in Dubai in 2024 that children would no longer necessarily have to learn programming languages, but should instead develop the ability to control and train AI systems. The new programming language is human. Anyone who is fluent in natural language can theoretically instruct AI systems to generate code, create images, or perform complex analyses. This democratizes access to technology, but at the same time makes traditional software skills a less scarce commodity. In the age of AI, the winner will no longer be the one with the most programmers, but the one with the best data, the highest computing power, and the deepest domain knowledge of the physical world.

This is precisely where Europe's structural advantage lies. While the US dominated the software era and China caught up through massive state investment in AI infrastructure and applications, Europe possesses something neither of them has: an industrial base cultivated over centuries, with a deep understanding of mechanical engineering, automation, manufacturing processes, and engineering expertise. This competence cannot be replaced by software. It is what physical AI needs to function in the real world. An autonomous robot in a factory must not only execute algorithms but also deal with precision mechanics, sensors, and the laws of physics. An AI-driven logistics system must not only optimize data but also move, stack, and sort real goods. A humanoid robot in healthcare must not only understand natural language but also interact gently and precisely with human bodies. All of this requires combining AI with excellent hardware, and that is precisely Europe's playing field.

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• Physical AI: When machines learn to touch the world, manufacturing faces its biggest transformation since the steam engine

Why Physical Artificial Intelligence Meets Europe's Industrial DNA

Europe's opportunity lies in physical AI, the fusion of artificial intelligence with robotics, automation, and industrial manufacturing. Jensen Huang succinctly stated this in Davos: Robotics represents a unique opportunity for Europe. The reason is structural. Physical AI requires not only digital intelligence but also excellent mechatronics, precision engineering, and deep domain expertise. These are fields in which Europe, and Germany in particular, has an unfair advantage. Siemens is the global market leader in digital twin technology, ABB and Schneider Electric dominate industrial automation, and German machine manufacturers like Trumpf, DMG Mori, and Dürr set global standards in production technology.

Integrating AI into these systems opens up a level of added value that extends far beyond software. At CES 2025, Siemens presented the Industrial Copilot for Operations, which brings AI directly to the production level, enabling operators and maintenance engineers to make real-time decisions. In collaboration with NVIDIA, the Teamcenter Digital Reality Viewer was announced, integrating large-scale, physics-based visualization into the Product Lifecycle Management system. Schaeffler is developing digital twins with NVIDIA for over one hundred plants to simulate and optimize materials, processes, and production workflows using AI. These projects demonstrate that Europe doesn't need to compete with OpenAI in AI model development, but can instead leverage AI as a tool to multiply its existing industrial strengths.

Robotics is the most concrete example. While China leads in the mass production of electric vehicles with companies like BYD, and the US dominates autonomous driving systems with Tesla, Europe holds a leading position in industrial robotics. Germany installed around 27,000 industrial robots in 2024, making it the fifth-largest robot market worldwide. Robot density in the European Union is 219 units per 10,000 workers, with Germany, Sweden, Denmark, and Slovenia among the top ten globally. Europe doesn't simply produce robots; it develops high-precision systems for complex manufacturing tasks that must meet the highest quality standards. This is a market where the best supplier, not the cheapest, wins.

In addition, there is the field of humanoid robotics, which is emerging as the next major growth market. Commerzbank estimates that the market for humanoid robots could grow to five trillion US dollars by 2050. Europe is positioning itself in this area with promising players. NEURA Robotics from Metzingen has established itself as the world's only company that develops and manufactures intelligent, cognitive robots entirely in-house. In January 2025, the company secured €120 million in Series B financing. Agile Robots from Munich develops systems that are no longer optimized for a single action but can solve tasks generically. Both companies benefit from the German engineering culture, which prioritizes precision, reliability, and safety.

The strategic importance of this development becomes clear when viewed in the context of the skills shortage. Germany and Europe are facing a demographic challenge. The number of people in the workforce is declining, while at the same time the demand for labor in industry, logistics, and care is increasing. Humanoid robots and AI-driven automation are not job killers, but rather necessary additions to maintain productivity. Huang emphasized this in Davos: AI creates more jobs than it destroys because every layer of the AI ​​infrastructure needs to be built and operated. From energy generation and chip production to data centers and application development, new fields of employment are emerging. The long-term economic benefits lie in the application layer, where AI is transforming industries such as healthcare, manufacturing, and financial services.

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The leapfrogging strategy as a response to structural speed disadvantages

The concept of leapfrogging, or skipping development stages, has been established in development economics for decades. It describes the phenomenon that countries or regions that have missed one technological stage can jump directly to the next without having to rebuild the outdated infrastructure. The classic example is telecommunications in Africa. Many African countries never had a comprehensive fixed-line network. Instead of building one, they jumped directly to mobile technology. Today, around 60 percent of the population in sub-Saharan Africa has internet access exclusively via smartphone. This number grew to 623 million users by 2025. The economic impact was enormous: Mobile banking with M-Pesa revolutionized financial transactions, e-commerce grew without traditional retail, and educational platforms reached remote regions without physical schools.

The logic of leapfrogging works when three conditions are met: First, the new technology must already be available and economically viable. Second, the old technology must be truly obsolete or economically unattractive. Third, it must be cheaper to jump directly to the new solution than to modernize the old one. For Europe, this means specifically: Instead of trying to compete with the US in building software platforms like Google, Meta, or Amazon, Europe should invest directly in integrating AI into physical systems. The software era is over, but the era of physical AI is just beginning. Whoever takes the lead now will set the standards for the coming decades.

A concrete example is warehouse logistics. European companies often still use semi-automated systems with manual order picking and simple conveyor systems. China, on the other hand, is building fully automated smart warehouses. JD.com uses over a thousand autonomous mobile robots in its logistics centers. Alibaba's Cainiao opened the largest smart warehouse in Southeast Asia in Thailand in 2025. These systems process millions of data points per second, predict bottlenecks, and optimize processes in real time. Instead of trying to modernize existing European warehouses step by step, Europe should build entirely new logistics centers with maximum automation, AI control, and autonomous robots. This is faster, more cost-effective, and avoids the path dependency of legacy infrastructure.

The same principle applies to other areas. In battery production, Europe currently holds only 13 percent of the global market, while China controls 70 percent. Instead of incrementally modernizing old technologies, Europe should invest in state-of-the-art gigafactories with the latest technologies and maximum automation. In microelectronics, Europe must implement modern production processes from the ground up, rather than renovating outdated chip factories. Regarding AI development, Europe should not try to copy generic Large Language Models like ChatGPT, but rather focus on industrial AI applications that combine domain knowledge with AI. This is precisely what the German initiative Next Frontier AI, announced by SPRIND in December 2025, is doing: Instead of entering the LLM race, Europe aims to leapfrog to the next frontier and develop new model classes, modalities, agentic systems, and more efficient training regimes.

Why Speed ​​Must Be Achieved Through Organizational Ambidexterity:
The central challenge for European companies lies not in a lack of technological competence, but in the speed of implementation. The concept of organizational ambidexterity describes the ability of organizations to be both efficient and flexible. It's about optimizing the core business—that is, exploiting existing products and processes—while simultaneously exploring and developing new business areas. This ambidexterity is crucial for remaining competitive in the long term in a rapidly changing world.

In practice, this means that companies must create parallel structures. One department focuses on exploitation, i.e., increasing efficiency and ensuring quality in day-to-day operations. These areas require formal structures, clear processes, and authoritative leadership to secure short-term successes. Another unit is dedicated to exploration, i.e., innovation and the development of new solutions. Here, agile organizational structures, visionary leadership, and room for experimentation are necessary. Both areas must be balanced by management so that the company neither becomes stifled by innovation nor stagnates in its operational business.

Studies show that 82 percent of executives worldwide believe their companies will not survive the next five years without new business models. At the same time, 57 percent of executives and 47 percent of knowledge workers view innovation projects as a luxury during the current economic crisis. This contradiction is fatal. In 62 percent of cases, the reason for this reluctance to innovate is the fear of failure and reputational damage. Added to this are outdated processes and technologies that hinder innovation. This is precisely where organizational ambidexterity comes in: it creates structures in which innovation is pursued systematically and not as a luxury.

For Europe, this means that companies must stop viewing innovation as a reaction to market events and instead proactively initiate transformation processes. The Franco-German Digital Summit in November 2025 demonstrated that this has been recognized. Germany and France announced 18 new strategic partnerships in the field of AI, with a total volume of over one billion euros. SAP, the largest European software company, announced a collaboration with the French AI provider Mistral AI. These are examples of how European forces are pooling their resources to gain speed. Individual countries are too small to compete globally. A European ecosystem, however, that combines strengths, can compensate for this speed disadvantage.

Why regulation can be used as a competitive advantage rather than a hindrance

One of the most frequent criticisms of Europe is its perceived overregulation, which stifles innovation. The European AI Act is often cited as an example of how Europe hinders itself while the US and China progress more quickly with fewer restrictions. However, this perspective overlooks a crucial point: regulation can become a competitive advantage when it sets globally accepted standards. Europe has successfully done this several times in the past. The General Data Protection Regulation (GDPR) became a global model for data protection laws. European product standards are adopted by many countries because they guarantee quality and safety.

Europe could play a similar role in the field of AI. While the US focuses on market-driven development and China on state-controlled systems, Europe could establish a third model: trustworthy, ethical, and secure AI. This is a market with enormous demand. Companies worldwide are looking for AI solutions that not only work but are also legally compliant, transparent, and explainable. Europe could set standards here and thus lead markets instead of just following them.

The prerequisite for this, however, is that regulation is designed not as a brake on innovation, but as a driver of innovation. This means regulatory sandboxes in which new technologies can be tested under controlled conditions without having to immediately meet all requirements. It also means a regulatory pause for experimental technology development, as successfully implemented in Rwanda and Kenya for drones and mobile payment services. These countries demonstrated that regulatory flexibility enables leapfrogging. Europe needs precisely this flexibility to be fast without compromising safety and ethics.

Why the next three years will determine Europe's position in the AI ​​age

The strategic challenge for Europe is not whether leapfrogging is possible, but whether the political and economic will to implement it exists. Jensen Huang's message in Davos was optimistic: Europe has a unique opportunity. But opportunities must be seized. The years 2024 to 2026 will determine whether Europe emerges as the leading market of the next industrial revolution or is relegated to the role of a mere hardware supplier.

The necessary steps are clear. First, Europe must invest massively in AI infrastructure. In February 2025, the European Union announced the InvestAI initiative, a €200 billion program with four AI gigafactories, each intended to house around 100,000 AI chips. This is a start, but the speed of implementation will be crucial. Second, Europe must strategically integrate its industrial base with AI. Siemens, ABB, Schneider Electric, and other European industrial giants are well-positioned, but they need partnerships with AI startups and access to computing power. Third, Europe must strengthen European partnerships. The Franco-German Digital Partnership is a model that needs to be extended to other countries. Fourth, Europe must take digital sovereignty seriously. Cloud data centers, AI gigafactories, and secure data platforms under European control are strategically essential.

The greatest danger is hesitation. While Europe debates, the US and China are building facts on the ground. Huang said in Davos that the world has only invested a few hundred billion dollars in AI infrastructure, but trillions are needed. The question posed by BlackRock CEO Larry Fink is therefore the right one: Are we investing enough? For Europe, the answer is currently: No. But the opportunity still exists if Europe stops chasing after others and starts shaping its own future using its own strengths.

The optimistic message is: Stop copying others, transform your own business model with the help of innovation, organizational ambidexterity, and AI. This is not capitulation, but a strategic realignment. Europe doesn't need to beat the US in software, but rather combine its industrial excellence with AI-powered automation. This is the second chance Jensen Huang described. It's up to Europe to seize it.

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