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The five-point plan: This way Germany wants to become AI world tip – data gigafactory and public orders for AI starups

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Published on: July 29, 2025 / update from: July 29, 2025 – Author: Konrad Wolfenstein

The five-point plan: This way Germany wants to become AI world tip – data gigafactory and public orders for AI starups

The five-point plan: So Germany wants to become AI world tip – data gigafactory and public orders for AI starups – Image: Xpert.digital

Germany's path to the AI nation: Can Europe exist in the global race?

Why is establishment as a leading AI nation for Germany of strategic importance?

The current global technology landscape is characterized by intensive competition in the field of artificial intelligence (AI), which is often described as a “AI race”. This race is primarily cited by the United States and China, which make massive investments in research, development and infrastructure. For a highly developed industrial nation like Germany, positioning in this field is not a mere option, but a strategic necessity. AI is no longer a niche technology, but develops into a fundamental basic innovation that will decide on future economic competitiveness, national security and geopolitical influence.

For Germany, the prosperity of which is largely based on its strength in key industries such as mechanical engineering, automotive industry and medical technology, a technological deficit in the AI sector is based on existential risks. A loss of technology leadership in these sectors would not only erode the economic basis, but also lead to critical dependence on foreign technology providers. The urgency of this challenge becomes clear in political strategy papers, which emphasize that time for crucial action is urging.

In response to this global dynamic, the German federal government has formulated strategic plans, the aim of which is to establish Germany at the “world leaders” of the AI nations. A central element of this strategy is a five-point plan of the Digital Minister, which outlines the essential fields of action to strengthen the AI location of Germany. This plan serves as a guide for a comprehensive transformation, which ranges from the targeted promotion of domestic start-ups to the establishment of a confident data infrastructure to the establishment of a value-based regulatory framework.

The analysis of this plan reveals a deeper strategic dimension. In view of the enormous investment gap between Europe and the USA or China, the German and European strategy cannot be an easy image of American or Chinese approaches. Rather, it is the design of an asymmetrical competitive strategy. This aims not to exist through sheer financial superiority, but through the intelligent use of more specific strengths: the close interlinking of AI with the strong industrial basis, the creation of a trustworthy, value -based ecosystem and the establishment of digital sovereignty as a quality feature. The following sections will analyze the five pillars of this strategy in detail and illuminate their implications, challenges and opportunities.

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Promotion of innovation through public allocation

What role does public contract award play in the promotion of AI start-ups in Germany?

A central lever to strengthen the domestic AI ecosystem lies in the strategic realignment of public order. The state acts in Germany as the largest of individual IT buyers, which means that the public sector awards orders in a three-digit billion dollar volume to private companies every year. This immense market volume is an important economic factor and harbors enormous potential for targeted promotion of innovation.

The current strategy criticizes the previous award practice as a “wild growth” and demands a targeted control of state digital expenditure. The core of the proposal is to strategically assign public orders to German and European AISTPUS instead of primarily awarding them to established, often US technology giants. This measure is intended to serve as a “innovation boost” by giving young, innovative companies a market entrance that they would otherwise find difficult.

However, reality shows that this potential has so far hardly been exhausted. Studies show strikingly low participation of start-ups in public tenders. Only about 11 % of German start-ups take part in such procedures, and only 7 % actually receive a surcharge. The proportion of public orders in the total turnover of these companies is correspondingly low; It is less than 5 %. This illustrates a significant discrepancy between the potential market that the state represents as a customer and the ability of start-ups to open up this market. The targeted award of public orders is therefore not only understood as financial support, but as a fundamental mechanism for opening the market and validating new technologies.

What hurdles do innovative young companies encounter in procurement law?

The low success of start-ups in public tenders is due to a number of specific bureaucratic and legal hurdles that are anchored in German and European procurement law. These hurdles are often tailored to the needs of large, established companies and represent insurmountable obstacles for young, agile companies.

One of the biggest challenges is the aptitude requirements. Public clients often require evidence of a certain minimum annual turnover, which may often be the two -time order value. This requirement is hard to meet for a start-up that is still in the growth phase and naturally has lower sales. In addition, there is the demand for comprehensive references via comparable projects from the past three financial years. This creates a classic “henne egg problem”: no references without public orders, and no public orders without references.

In addition, the complexity and the duration of the award procedure scare many start-ups. The creation of the offer documents is time and resource-intensive, which is a significant burden for small teams. The procurement law itself is characterized by a high regulatory density and a division of regulation: orders below certain EU threshold values are subject to national regulations such as the subjecty agreement (UVGO), while orders above these values must be advertised across Europe and are subject to more complex regulations such as the law against competitive restrictions (GWB) and the award regulation (VGV). This legal complexity increases the entry hurdle and leads to many innovative companies avoid the public sector as potential customers from the outset.

Which solutions and reforms are discussed in order to facilitate access to public orders?

In order to reduce the hurdles described, various solutions are discussed at the legal and political level. These aim to make the procurement law more flexible and more innovative -friendly without giving up the basic principles of transparency and competition.

At the legal level there are already instruments that can use start-ups to compensate for their disadvantages. This includes the formation of “bidding communities”, in which several smaller companies join together to jointly raise the capacities for a larger order. Another option is the “aptitude loan”, in which a start-up “Lend”, such as references or sales figures, from an established partner company, which in return is obliged to provide its resources in return.

At the political level there are comprehensive reform proposals, such as the 7-point plan of the digital association Bitkom. Among other things, this requires a stronger application of existing innovative award criteria, the creation of new evaluation standards tailored to start-ups and harmonize the rugged legal frames. A central point is the professionalization of the procurement points. The employees in the awarding authorities need the specialist knowledge in order to be able to assess innovative AI solutions, which often requires specialization and targeted training. Another important instrument is the “innovation partnership”. This is a special award procedure that is explicitly designed to develop an innovative solution together with a company that is not yet available on the market. It is therefore ideal for the procurement of new AI technologies and promotes the cooperation between public hands and innovative providers.

The following table summarizes the central challenges and the corresponding solutions:

Innovation instead of a low price: new opportunities for start-ups for orders

Innovation instead of a low price: new opportunities for start-ups for orders

Innovation instead of a low price: new opportunities for start-ups for orders – Image: Xpert.digital

Start-ups are in front of various hurdles for orders that can enable new opportunities through innovation instead of low price. Strict aptitude criteria such as minimum sales and references often exclude young companies from the competition due to a lack of corporate history. Solutions such as the use of aptitude lending, the approval of personal references of employees and the adaptation of the criteria to the respective corporate phase could help here. The high complexity and duration of the procurement procedures overwhelm small teams and cause great resource effort, which is why a reduction in bureaucracy, the digitization of the award procedures (such as through eViels) as well as targeted training and networking of start-ups would make sense. The often inappropriate order size, when the lack of lots exceeds the capacity of small companies, can also be improved by the consistent application of the medium -sized business clause (§ 97 GWB) for the distribution of orders into loose and the promotion of bidding communities. Another crucial point is the focus on the lowest price, the innovative but potentially more expensive solutions. The introduction of an "innovation premium" as a surcharge criterion, the broader use of functional performance descriptions and the use of innovation partnerships can open up new opportunities here. Finally, a lack of transparency and lack of feedback complicates the learning process for start-ups and prevent improvements in future offers. The publication of comprehensive award statistics and mandatory feedback for bidders that have not been taken into account would support this process.

What economic consequences does the targeted preference for domestic companies have?

The strategic intention to prefer public orders to “domestic AI companies” represents a form of industrial policy, which, however, is in a tension between established economic principles and the European legal framework. The core of this area of tension lies in the conflict between the promotion of a national technology ecosystem and the potential loss of efficiency due to limited competition.

EU giving rights are based on the basic principles of the internal market: transparency, equal treatment and non-discrimination. These principles are intended to ensure that the most economical offer is awarded the contract, regardless of the bidder's national origin. This open competition is considered an important engine for economic growth and, according to estimates, contributes significantly to GDP of the EU. A policy that explicitly prefers domestic companies undermines this principle and risks to violate EU law.

From an economic point of view, such a protectionist measure can lead to higher prices for the public sector. If the competition is artificially restricted by excluding international providers, the remaining domestic bidders can enforce higher prices. Studies on the effects of local preference in procurement system indicate that this can increase the costs for taxpayers and reduce the efficiency of public expenses.

In contrast, the industrial policy arguments are. The supporters of such a strategy argue that temporary preference is necessary to give a young, strategically important industry like the AI industry a fair opportunity in global competition. A state mandate can act for a start-up as a decisive “first customer” that not only generates sales, but also serves as an important reference and thus facilitates access to private markets and other venture capital. It is therefore a strategic consideration: at short notice higher costs and potential efficiency losses are accepted in order to build up a sovereign and competitive domestic technology in the long term and to avoid critical dependencies. The implementation of this strategy therefore requires a careful balancing act to promote domestic industry without endangering the cornerstones of the European internal market.

 

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Germany in the AI race: The key to national arithmetic performance infrastructure and innovation promote despite the strict regulation and bureaucratic hurdles

Building a national computing power infrastructure

What is the current state of the data center infrastructure in Germany and why is it crucial for AI?

The computing power is the fundamental backbone of the digital economy and is the indispensable resource for the development and operation of modern AI applications. Large AI models, in particular basic models, require immense computing capacities for training, the billions of parameters and huge amounts of data. Without a powerful and scalable infrastructure of arithmetic and data centers, the ambition to become a leading AI nation cannot be feasible.

Germany currently has the largest data center capacities within Europe. The Frankfurt am Main location has established itself as a central hub, which is largely due to the DE-CIX located there, one of the world's largest internet nodes. This concentration ensures excellent connectivity and attracts investments from global cloud providers and colocation service providers.

Despite this leading position in Europe, a relative view shows a more differentiated picture. If you compare the available computing power to economic strength, measured by the gross domestic product (GDP), Germany falls behind other nations. Countries such as Great Britain or the Netherlands have a higher density of computing power per billion euros in GDP. In global comparison, the distance to the USA and China that dominate the market is even clearer. This relative gap signals a potential bottleneck that could restrict Germany's ability to keep up in the global AI race. The country's digital sovereignty and technological ability to act directly depend directly on the strength and expansion of this critical infrastructure.

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What does the demand for a “gigafactory for data” mean in the context of the AI strategy?

The term “gigafactory”, originally shaped by Tesla for its huge factories for mass production of batteries, is used as an effective metaphor as part of the German AI strategy. The demand for “at least one gigafactory” in Germany is not literally understood as a single factory, but as a political commitment to building data centers in hyperscale format, which are specially designed for the extreme requirements of AI applications.

A “Gigafactory for Data” symbolizes a qualitative and quantitative leap in the national arithmetic infrastructure. It is no longer just about the operation of conventional data centers for standard cloud services, but about the creation of systems that are able to cope with the most computing tasks – above all the training of AI-based models with trillions of data points. Such systems require a massive concentration of specialized hardware (especially GPUS), an extremely high energy density and highly developed cooling systems.

The requirement implies the strategic need to create a sovereign arithmetic infrastructure that enables German and European companies to develop and operate AI models in their own country. This reduces the dependence on the cloud platforms of American Hyperscaler and strengthens digital sovereignty. The “Gigafactory” is therefore the physical foundation for the ambition to become an independent “cloud nation” and to be able to survive in the global competition for technological leadership at AI.

What are the biggest challenges in expanding the data center capacities in Germany?

The ambitious plan to massively expand national computing power comes across a number of considerable physical, regulatory and social challenges. These bottlenecks show that the digital transformation fails due to very concrete, non-digital boundaries if they are not proactively addressed.

The biggest challenge is energy supply. Data centers, and especially those for AI applications, have enormous and steadily growing power consumption. The energy requirements of the German data centers could almost double until 2030 compared to today. This collides with the high energy prices in Germany, which in international comparison represent a significant competitive disadvantage and can make investments unattractive.

A second major obstacle are the lengthy planning and approval procedures. In Germany, it takes significantly longer than in the EU average to approve and build a new data center. These bureaucratic delays create investment uncertainty and slow down the urgently needed expansion of the infrastructure.

Thirdly, the high area of space from data centers increasingly leads to land use conflicts. The construction of large server farms on arable land or near residential areas encountered resistance from farmers, conservationists and residents who fear area sealing and noise pollution.

Finally, sustainability is a central challenge. Data centers produce a huge amount of waste heat, which is usually released to the environment unused. Although there are legal requirements for the use of waste heat, the practical implementation often fails due to the lack of infrastructure, such as connected district heating networks. This leads to a trilemma between the goal of AI leadership, the energy transition and the climate protection goals. The expansion of the AI infrastructure can endanger the climate goals if it is not embedded from the start into an integrated energy and urban development strategy.

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Bureaucratic reduction and the free flow of data

In which area is the demand for a unhindered data flow for AI applications?

The requirement to reduce bureaucracy so that data can flow unhindered is a central but also highly complex point of the AI strategy. It affects the nuclear voltage field of the European approach to digitization: the conflict between the unconditional need for large amounts of data to promote innovation and the equally unconditional confession of strict data protection to protect fundamental rights.

Artificial intelligence, especially machine learning, is data driven. The performance and accuracy of AI models depend directly on the amount and quality of the data with which they are trained. From the point of view of technology development, the most free and uncomplicated access to huge amounts of data is therefore a basic requirement in order to be able to exist in global competition. The demand for a “flowing” data traffic is therefore a plea for innovation -friendly framework.

However, this innovation imperative is offset by the European legal framework, which is characterized by the General Data Protection Regulation (GDPR). The GDPR is not designed as an innovation brake, but as a framework for the protection of fundamental civil rights. It is based on principles such as data minimization (only as few data should be processed as necessary), the purpose binding (data may only be used for the purpose for which they have been collected) and the need for a clear legal basis for any data processing, often in the form of informed consent. These principles are in a natural tension between the “data hunger” of AI development, which leads to considerable legal uncertainty among companies and researchers.

What specific bureaucratic and legal hurdles exist for AI developers in the field of data protection?

For AI developers in Germany and Europe, the area of tension between data requirements and data protection manifests itself in a number of specific legal and bureaucratic hurdles that result directly from the GDPR and its interpretation.

The principle of data minimization represents a fundamental challenge. While the GDPR requires the processing of personal data to limit the level necessary for the purpose, many advanced AI models are based on the analysis of huge, unspecific data records in order to recognize patterns. The “data hunger” of the AI is in direct contradiction to the required data economy.

The hurdle of purpose is closely linked. According to GDPR, data may only be collected for defined, clear and legitimate purposes. However, the training of AI base models is often carried out for a variety of potential and at the time of training not yet foreseeable future applications. This makes the definition of a specific purpose and creates legal gray areas.

Another major hurdle is the requirement of a lawful processing basis. For the training of AI models with personal data that is often collected from the Internet, it is practically impossible to obtain explicit and informed consent from each individual. Developers therefore often refer to the “legitimate interest”, but its reach is legally controversial and is increasingly interpreted by data protection authorities, which leads to considerable legal uncertainty.

Finally, the often non-transparent functionality of complex AI systems, the so-called “Blackbox” problem, collides with the transparency obligations of the GDPR. Citizens have the right to information about the logic that is behind automated decisions. If even the developers can no longer understand the exact decision paths of a deep learning model, this right is hard to guarantee. In total, these hurdles result in AI development in Europe associated with a higher legal risk and greater bureaucratic effort than in other world regions.

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How does the European AI Act try to create a balance between innovation and regulation?

The European AI law is the most comprehensive attempt to create a regulatory framework that makes the risks of AI mastered without choking the innovation. It is the central answer to the area of tension described and embodies a strategic decision for a third way between the Laissez-Faire approach of the USA and the state-controlled AI development in China.

The core of the AI law is its risk-based approach. Instead of regulating AI in general, the law differentiates according to the potential risk of damage to an application. AI systems with an “unacceptable risk”, such as state social scoring or manipulative techniques that influence people's behavior, are fully prohibited. Systems with “high risk” that are used in critical areas such as medical diagnostics, personnel recruitment or the judiciary are subject to strict requirements for transparency, data security, human supervision and documentation. The vast majority of AI applications, which are classified as low-risk, such as spam filters or AI in video games, remain largely unregulated.

At the same time, the AI law contains explicit mechanisms for promoting innovation, which are particularly aimed at start-ups and small and medium-sized companies (SMEs). The most important instrument is the so -called “regulatory sand boxes”. These are controlled legal experimental spaces in which companies can develop and test innovative AI systems under the supervision of the responsible authorities without having to expect the full sanctions of the law immediately in the event of unintentional violations. These sand boxes are intended to create legal and planning security, facilitate market access and promote dialogue between innovators and regulators. The AI law is therefore not only a protection instrument, but also a strategic attempt to create a reliable and trustworthy framework that is intended to direct innovations and serve as a competitive advantage in the long term.

 

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Europe's path to digital sovereignty through its own AI-based models: EU-KI law as a competitive advantage in international technology

European sovereignty in AI base models

Why is the development of your own European AI base models of strategic importance?

The development and control of AI base models, also known as the basic models, has become a question of central strategic importance for the future of Europe. These models are the technological basis on which a variety of future AI applications will build. A complete dependency on models that are developed and controlled exclusively by companies in the USA or China carries significant risks for the “digital sovereignty” of Europe.

Digital sovereignty describes the ability of states, companies and citizens to self -determine their digital transformation and to avoid critical technological dependencies. If the basic AI infrastructure lies in the hands of non-European actors, diverse risks arise. First, there is an economic dependency that can lead to unfavorable conditions or restricted access to key technologies. Second, data that is processed on US cloud platforms are potentially subject to access by US authorities within the framework of laws such as the cloud act, which contradicts European data protection ideas.

Third and perhaps most importantly, the fact that AI-based models are not value-neutral. They are trained with data that reflect cultural, social and ethical ideas. Models that are primarily trained with data from the American or Chinese cultural area can contain bias (bias) that are not compatible with European values and norms. The development of own European basic models is therefore essential to ensure that the AI builds up the future on a foundation that respects European basic values such as democracy, the rule of law and the protection of fundamental rights. Initiatives such as Gaia-X, which are supposed to create sovereign European data infrastructure, are an important component in this way.

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What is the current status of developing AI base models “Made in Europe”?

Despite the considerable investment deficit towards the USA and China, a dynamic scene has established itself in Europe for the development of AI-based models, which pursues its own, differentiated strategy. Instead of trying to build the largest and most powerful all -purpose models, many European actors focus on specific niches and quality features.

A leading German company in this area is Alph Alpha. The Heidelberg start-up specializes in developing AI models that are not only efficient, but also transparent and understandable (“explainable AI”). This focus on trustworthiness and sovereignty makes Alph Alpha an important partner for the public sector and regulated industries. The company has recently adapted its strategy and is now focusing more on smaller, specialized models for specific areas of application, which is seen as a strategic departure from direct competition with global hyperscalers.

Another European hope is the French company Mistral Ai, which has received great attention from the publication of powerful open source models. The open source approach promotes transparency and enables a broad community of developers to build on and adapt them on the technology.

In addition, there are state-funded initiatives such as Opengpt-X, a project with the participation of Fraunhofer Institutes that drives the development of open and trustworthy language models for Europe. At the University of Würzburg, “Llämmlein” was also developed by “Llämmlein” the first large language model trained purely on German data in order to break through the dominance of English -language training data and to improve the quality for the German language. These examples show a clear strategic orientation: Europe does not primarily compete with the sheer size of the models, but through specialization, openness, transparency and adaptation to the specific linguistic and regulatory needs of the European market.

What role does EU regulation, in particular the AI law, play in the global competition of the AI models?

The European regulation, especially the AI law, plays an ambivalent and much-discussed role in the global AI competition. On the one hand, the concern is expressed against “over-regulation from Brussels” that could burden European developers with high compliance costs and bureaucratic hurdles and thus let them fall behind compared to agile competitors from the USA and China. Critics fear that strict regulations are slowing innovations and, especially for start-ups, could be a market entry barrier.

On the other hand, the AI law is increasingly understood as a strategic instrument that can create competitive advantages in the long term. By establishing the world's first comprehensive legal framework for AI, the EU creates legal and planning security for companies and users. This clear framework can attract investments and strengthen trust in AI applications. The law also explicitly takes into account the needs of SMEs and start-ups by providing innovation-friendly instruments such as the regulatory sand boxes already mentioned and differentiating them with the fines according to company size.

Perhaps the most important strategic function of EU regulation lies in the so-called “Brussels Effect”. Since the European internal market is indispensable for global technology companies, they will be forced to adapt their products and models to the strict EU requirements in order to be able to work here. In this way, the EU exports its regulatory standards and value -based ideas of Ki de facto all over the world. The regulation thus becomes a powerful instrument of global design from a potential burden. Instead of competing in a pure technology competition, which Europe could possibly lose due to investment gaps, the EU relocates the competition at the level of governance models, where it takes a leadership position through a clear, value-based and comprehensive legal framework.

International cooperation and AI according to European values

What does the claim mean to develop a AI according to “European values”?

The aim of developing artificial intelligence according to “European values” is a central leitmotif of the German and European digital strategy and the decisive differentiation factor in global competition. It is less about specific technical architecture than the embedding of AI systems in a robust legal and ethical framework, which reflects the fundamental rights and democratic principles of Europe.

This value-based approach is most clearly specified in the EU Ki Act. The principles anchored in it define what makes a “European AI”: it must be human -centered, which means that man always has to keep the last control instance (human supervision). It has to be safe, robust and transparent, so that your decisions are understandable and it cannot be easily manipulated. A core principle is non-discrimination, which requires that AI systems do not intensify or create any existing social prejudices (BIAS). Protecting privacy and data sovereignty is another cornerstone due to the close link with the GDPR. Finally, aspects such as social and ecological well-being are named as goals for AI systems.

In practice, this approach manifests itself through clear bans and strict requirements. AI applications that fundamentally contradict the European values, such as state social scoring based on the Chinese model or systems for unconscious manipulation of behavior in the EU. For high-risk applications, strict requirements apply that should ensure that these systems act fair, safe and transparently. “AI according to European values” is therefore a political and social project that is inextricably linked to the protection of fundamental rights and democratic processes.

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How can an “exchange at eye level” be designed with technology leaders like the USA?

The demand for an “exchange at eye level” with technology leaders such as the United States is an expression of striving for digital sovereignty. It implies a departure from the role of a pure technology consumer and controller to that of an active and equal designer of global digital order. Several factors are crucial to achieve this position.

First, “eye level” requires its own technological skills. Only those who have relevant AI models, research capacities and a strong start-up ecosystem are perceived as a serious partner in technological dialogues. The efforts described in the previous sections to set up their own AI industry and infrastructure are therefore the basic requirement.

Second, “eye level” is based on the strength of the European internal market. As one of the largest and most powerful economic areas in the world, the EU can throw its market power into the balance as a political weight. Global companies are dependent on access to the European market, which gives the EU a strong negotiating position in determining standards and rules.

Third and crucial, “eye level” is created by its own coherent and globally influential regulatory framework. The AI law is the central instrument here. It defines a clear European point of view and forces international partners to deal with the European ideas of a value -based AI. Instead of just reacting to American or Chinese standards, Europe proactively sets its own. The aim is to prevent Europe from being “divided” by the USA technologically and regulatory by appearing as a closed block with a clear, own agenda.

What strategic implications result from the global race of the regulatory systems?

The global competition for leadership role in artificial intelligence is not only a race for technologies and investments, but increasingly also a competition for the regulatory systems and the associated social visions. Three distinct models crystallize, each set different priorities.

The European model, anchored in the AI law, is a comprehensive, risk-based and fundamental approach. It prioritizes security, trust and ethical guardrails and tries to steer innovation within a clearly defined legal corridor. His goal is to become a global role model for a responsible AI government.

The US model is traditionally more market-oriented and innovation. The focus is on the minimization of regulatory hurdles to accelerate the technological development and commercial exploitation of AI. Regulation is often reactive and sector -specific instead of a comprehensive, preventive legal framework. The strategy aims to secure technological supremacy through maximum freedom for the leading companies.

The Chinese model is steered by the state and geared towards achieving national strategic goals. Regulation is agile and can quickly be adapted to new technological developments, but also serves to strengthen state control and surveillance. Innovation is massively promoted by the state, but always in accordance with the political goals of the government.

The strategic implication for Germany and Europe is that your own, value -based approach must be actively positioned as a strength and as a global unique selling point. In a world that is becoming increasingly aware of the potential risks of AI, the “trustworthy AI” label can become a decisive competitive advantage. The success of the European strategy will depend on whether this regulatory framework is not possible as an innovation brake, but as a seal of approval for safe, fair and high-quality AI systems that find demand worldwide – especially in critical and sensitive areas of application.

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Digital Pioneer – Konrad Wolfenstein

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