Germany's AI goals by 2030: Eighteen measures won't create a single profitable AI market – What companies really need

More AI, but too little impact? What matters now for Germany's economy

German government's AI initiative: How funding will become a real market

Germany aims to become a leading AI nation by 2030. The German government has formulated 18 key measures and ambitious goals to achieve this: AI should be used significantly more frequently in companies and public authorities and make a greater contribution to economic output. However, political decisions, funding programs, and additional data centers alone do not guarantee economic success. The crucial factor is whether companies become measurably more productive with AI, whether public authorities can better fulfill their tasks, and whether technology providers can develop sustainable business models from pilot projects.

This is precisely where the challenge lies. The use of AI is increasing, but there is often a gap between initial experimentation and deep integration into workflows. Small and medium-sized enterprises (SMEs) in particular face high costs, limited human resources, and complex issues surrounding data, security, and technical integration. At the same time, the government must clarify where it can act as a reliable customer and supporter – and where new programs merely duplicate existing offerings or create dependencies.

This article examines Germany's AI strategy from an economic perspective: What is hindering the transition from application to value creation? What role do public demand, European suppliers, and industrial collaborations play? And how can we determine whether funding measures are actually effective? The central thesis: Success depends not on the number of measures, but on whether they eliminate specific bottlenecks and enable measurable, sustainable results.

Germany's AI strategy put to the economic test: How political measures create competitive markets

Germany doesn't need the most extensive collection of AI programs possible. It needs companies that become more productive with artificial intelligence, public institutions that perform their tasks more effectively, and technology providers that develop viable business models from repeatable solutions. This is precisely what will determine whether a national AI strategy is economically successful. The number of political measures is no more a reliable measure of this than the number of funded projects or the size of announced data centers.

With its vision of becoming an AI-driven nation by 2030, the German government has consolidated 18 key cross-departmental measures. Among the publicly stated goals are the use of AI by at least 80 percent of large companies and 55 percent of small and medium-sized enterprises (SMEs). Furthermore, 90 percent of federal agencies are to use at least one AI application for their core tasks. More than ten percent of economic output is to be generated using AI. These objectives are ambitious. However, whether they actually describe an economic transformation depends significantly on how usage, value creation, and success are defined.

A company can deploy a text assistant and thus be considered an AI user. This doesn't automatically mean that its production costs will decrease, its sales will be more successful, or its innovation rate will increase. A government agency can support a single process with AI while the vast majority of its processes remain unchanged. And AI-based economic performance can only be meaningfully measured if a distinction is made between the revenue of AI providers, AI-supported activities, and the actual additional value created.

The crucial economic question is therefore not whether Germany has adopted enough measures. It is whether these measures address the right bottlenecks. These include a lack of demand, insufficiently processed data, high integration costs, uncertain business models, and an excessively slow transition from research results to marketable products.

An effective AI policy must address these problems differently. It must neither declare every entrepreneurial difficulty a case for funding nor rely on the assumption that a market without suitable infrastructure and reliable framework conditions will automatically produce internationally competitive providers.

The real gap lies between initial use and economic penetration

The available business data reveals an important development: AI is no longer solely an experimental topic in Germany. By 2025, 26 percent of the surveyed companies were using artificial intelligence. For small businesses with 10 to 49 employees, the figure was 23 percent, for medium-sized businesses with 50 to 249 employees it was 36 percent, and for large businesses with 250 or more employees it was 57 percent. These figures refer to the statistically recorded size categories and cannot be directly extrapolated to all micro-enterprises and self-employed individuals.

This resulted in a 34 percentage point gap between small and large companies. This is more economically significant than simply looking at the average usage rate. Large companies can spread the costs of data preparation, technical integration, legal review, and employee training across more employees and business processes. They also more frequently have their own IT departments and sufficient financial reserves to test multiple use cases.

For a smaller company, the same entry into AI can represent a significantly greater organizational burden. Management must not only assess the technology but also select providers, clarify data access, and ensure the security of ongoing processes. Even if an AI application functions technically, its implementation can fail due to a lack of time and unclear responsibilities.

The type of use is also crucial. Among companies using AI, 27 percent employed the technologies for workflow automation or decision-making in 2025. Six percent used them for the autonomous movement of machines. These figures explicitly refer to companies already using AI, not to the entire business population. They demonstrate that a general usage rate and deep integration into operational processes are distinct concepts.

This clarifies the need for economic policy action: Germany must not only introduce more companies to AI. It must facilitate the transition from occasional use to robust, repeatable, and economically relevant applications.

A text-based tool can be a useful starting point. However, greater productivity gains often only occur when information flows, responsibilities, and operational processes are also changed. This second step is precisely the more demanding, expensive, and less convenient for political success statistics.

Why small initial budgets don't finance industrial implementation

The discussion about low-funded AI projects touches on a real cost-cutting problem. There are significant differences between a brief orientation tool and a production-ready application. A budget of, for example, €2,000 might suffice for a clearly defined workshop, an initial potential analysis, or the setup of a simple standard tool. However, it is not a realistic funding framework for a customized, integrated solution with demanding security requirements.

An example calculation illustrates the scale of the costs. Assuming an external daily rate of €1,000, such a budget covers two working days. From this, preparation, coordination, execution, and documentation must be paid for. This leaves little room for a thorough process analysis, multiple system integrations, a robust testing phase, and training for various user groups. This calculation is for illustrative purposes only and does not represent a general market price.

The problem, therefore, doesn't necessarily lie in the existence of small initial budgets. The problem arises when an orientation tool is confused with an implementation tool. Anyone expecting a comprehensive transformation with a small amount of money creates disappointment on both sides: the customer doesn't receive a productive solution, and the provider cannot deliver a viable service.

A well-designed support model would need to consider different levels of maturity. The first question to address is which use case makes economic sense. This is followed by a limited test with clearly defined success criteria. Only then should integration into ongoing operations be financed. Scaling to additional locations, departments, or customers is a separate task altogether.

Crucially, what happens after the initial funding is crucial. A one-off consultation doesn't create a sustainable market in the long term. Only when companies are willing to continue funding a solution with their own resources will there be a reliable indication of its economic benefits.

Public support should therefore not aim to generate as many non-binding initial contacts as possible, but rather to facilitate the transition to self-financed follow-up investments. The success of an entry-level program would then not be measured by the number of vouchers issued, but by productive applications, their continued use, and their demonstrable contribution to the company's bottom line.

For young AI companies, demand is often more valuable than additional project funding

For a technology provider, funding and customer revenue are fundamentally different. Funding finances specific development work. A paying customer, on the other hand, confirms that a concrete service provides sufficient benefit under real-world conditions.

This difference impacts the entire business model. Recurring revenue enables workforce planning, improves negotiating position with investors, and provides insights into which features customers actually need. A successfully completed development project can be helpful, but a consistently used product is the stronger foundation for growth.

Especially in the AI ​​market, there's a risk that companies conduct too many individual demonstration projects without building a scalable offering. Each new customer then receives a custom solution. Revenue may grow, but personnel costs increase at the same rate. The company remains economically a project-driven service provider, even though it presents itself externally as a scalable software provider.

This isn't inherently bad. Specialized consulting and integration can also be valuable business models. However, from an economic policy perspective, a clear distinction must be made between programs that finance services, create a standardized product, or support the development of a fundamental technology.

For scalable AI providers, it is particularly relevant whether a solution can be deployed to other customers with significantly less additional effort. Reusable interfaces, standardized security features, and clearly defined operating models are often more important in this regard than another spectacular demonstration.

An economically oriented AI policy should therefore not only promote development activities. It must also improve the conditions under which pilot customers become repeat customers. This includes clear requirements, reliable payment terms, transferable technical standards, and demand that does not disappear when a program ends.

The state, as an anchor customer, must be reliable without creating new dependencies

The German government explicitly describes the state as a potential anchor customer for AI models and applications. This is based on a logical economic mechanism: Public institutions need high-performance digital tools, while young providers require solid demand, practical experience, and references. If both sides are brought together effectively, a self-reinforcing market can emerge.

However, simply declaring the state the anchor customer is not enough. Its demand must be predictable and economically manageable for suppliers. If each institution pursues its own proprietary technical solutions, adaptation costs multiply. A supplier may gain individual customers, but not a scalable foundation.

Common interfaces and reusable components can mitigate this problem. A solution to support document processing should not have to be completely redesigned for each agency. At the same time, different professional requirements must be taken into account. Standardization therefore does not mean making all administrative processes the same, but rather creating common technical foundations.

The state must balance two interests. On the one hand, it can be beneficial to strategically develop European suppliers as serious market participants. On the other hand, this must not lead to a permanent guarantee of purchase for economically or technically inadequate products. A decision regarding origin does not replace a performance evaluation.

Even a European provider can create a strong dependency among its customers. Closed data formats, difficult-to-transfer customizations, and unclear switching options remain problematic, regardless of the company's location. Therefore, sovereign public use of AI requires not only a specific geographical origin but also interchangeability and control.

The government would be a good anchor customer if it sets demanding, realistic requirements, reliably uses successful solutions, and simultaneously ensures that switching providers is technically possible at a later date. This creates demand without sacrificing competition and the pressure to innovate.

Mistral is a beacon of hope for Europe, but not a complete sovereignty strategy

Mistral exemplifies the question of whether Europe can build its own high-performing AI providers. The company develops models and increasingly combines them with offerings for enterprise use. Together with SAP, it is working on a sovereign AI platform for European business customers. This aims to consolidate model development, enterprise software, and controllable operating environments.

From an economic perspective, this connection makes sense. A powerful model alone doesn't solve a business problem. It requires access to relevant data, integration with existing software, and clear rules for permissions, logging, and responsibility. An established enterprise software provider can offer significantly better market access in this regard than an isolated model provider.

Nevertheless, it would be risky to equate European AI sovereignty with the success of a single company. A nationally or European-favored provider may be strategically important, but it remains a company with its own economic interests, technological limitations, and financial risks.

Furthermore, an AI system consists of multiple layers. Even a European model can be trained on non-European hardware, depend on specific accelerator chips, or operate via an infrastructure whose essential components are controlled outside of Europe. Sovereignty is therefore not a binary property.

Rather, it should be measured by the existing dependencies, which of these are economically acceptable, and for which critical functions alternatives remain available. A European model increases the choice. However, it does not automatically eliminate all other dependencies.

The objectively justified position is therefore: Europe should develop and deploy high-performing providers like Mistral as a genuine competitive option. At the same time, multiple providers, open operating options, and technical switching possibilities are needed. A legitimate industrial policy interest should not lead to a new single-provider strategy.

Industrial AI gains traction where it solves concrete process problems

Germany's industrial base offers a crucial starting point for economically relevant AI. The advantage, however, lies not simply in the abundance of machinery. It lies in the interplay of expertise, process understanding, customer relationships, technical data, and years of experience with complex systems.

The collaboration between Airbus and Mistral illustrates this logic. The identified application areas include industrial processes, technical documentation, engineering, design, and AI-supported assistance during development, testing, and certification phases. This is not a single, general application, but rather a range of distinct tasks, each with its own specific quality and safety requirements.

Particularly interesting are tasks where specialists spend a lot of time searching for, compiling, and verifying information. While faster evaluation of technical documents doesn't automatically lead to a new product, it can accelerate development work and free up scarce expert capacity for more demanding tasks.

The same applies to intralogistics. An AI application could structure fault reports, consolidate maintenance information, or support the analysis of recurring plant problems. The benefit then depends less on impressive speech processing capabilities than on the quality of the connection to real plant and process data.

Different technical tasks must be kept separate. A language model can be helpful for documentation and knowledge access. For the direct control of a machine or a safety-critical decision, different requirements must be met. Not every industrial AI application is a generative language system.

Existing partnerships between industrial companies, software providers, and model developers can therefore be more valuable than an additional, abstract funding program. However, their effectiveness must be measured by concrete results. A well-known corporate group is not, in itself, proof of a working solution.

Furthermore, a successful industrial ecosystem should not only include large corporations. Specialized medium-sized businesses, integrators, and smaller software companies must have access to interfaces and markets. Otherwise, closed platforms will emerge, whose benefits will only reach a limited extent across the economy.

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The productivity gain only arises after deducting all additional costs

Many AI discussions focus solely on the time saved during processing. This is insufficient for an economic evaluation. The crucial question is what benefit remains after considering all additional costs and risks.

A simplified example illustrates the difference. A company with 100 employees saves ten minutes per person per day by using this application. With 220 working days, this equates to approximately 3,667 hours per year. Assuming total costs of €50 per hour, this results in a calculated potential time value of about €183,000 annually.

However, this amount of time does not automatically translate into additional profit or a corresponding saving in payment transactions. If the freed-up minutes are scattered across many tasks, they may only be partially used productively. Furthermore, there are licensing costs, implementation costs, support, and monitoring expenses.

Assuming annual operating costs of €60,000 and only half of the calculated time potential is actually realized, the annual benefit before considering initial implementation costs would be approximately €31,500. This example is deliberately hypothetical to illustrate how significantly an initially impressive gross figure can change upon closer examination.

In quality-critical tasks, errors not only cost time. Inaccurate technical information can trigger rework, delays, or liability risks. Conversely, improved error detection can create significant benefits that are not immediately quantifiable in minutes.

Companies therefore need an evaluation that combines time, quality, speed, and risk. Meaningful metrics include, for example, the processing time of a process, the number of necessary corrections, the utilization of scarce skilled workers, or the cost per case processed.

Political programs should support this perspective instead of rewarding the highest possible theoretical savings potential. What is economically crucial is not how quickly an AI generates a design, but how efficient the entire process becomes, leading to a usable result.

European cooperation is a matter of scale, not a diplomatic competition

The juxtaposition of a stronger orientation towards France and cooperation with Canada is too simplistic if it is understood as the only option. The German government cites both European capabilities and international partnerships as components of its approach. In this context, cooperation with Canada and support for LawZero in security-oriented AI development are highlighted, among other things.

From an economic perspective, every partnership must be evaluated according to its specific contribution. France is a natural partner for Germany in building a larger European market and in collaborating with European model providers. Canada can contribute additional expertise in certain research and technology fields.

The relevant question, therefore, is not which country receives more political attention. It is whether cooperation opens up access to technology, skilled workers, customers, or complementary knowledge. A partnership without operational links remains symbolic. A smaller but productive collaboration, on the other hand, can generate measurable benefits.

At the same time, international cooperation should not obscure the fact that Europe must solve its own size problems. Differing technical requirements and separate national markets can mean that a European supplier has to make significant adjustments for each new customer group.

For a growing AI company, a genuinely usable European market is therefore often more important than another political cooperation framework. Common requirements, transferable proofs, and compatible infrastructures can enable scaling without completely eliminating national specificities.

A smart strategy therefore combines two levels: It pools European demand and capabilities where size is crucial, while remaining open to international partners who offer concrete technological complements. Openness and strategic independence are not mutually exclusive, as long as dependencies are consciously assessed.

Chip expertise requires specialized infrastructure and a clear division of labor

The desire for greater independence in AI chips is understandable. However, it becomes imprecise as soon as chip design, semiconductor research, manufacturing, and the operation of AI data centers are treated as a single task.

A chip must be designed. Its design requires suitable tools and technological foundations. Manufacturing, in turn, requires specialized equipment, materials, and processes. Added to this are storage, packaging technologies, and integration into complete computing systems. Advances at one level do not automatically compensate for missing capabilities at others.

The Belgian research center imec is a key European hub in this context. The NanoIC pilot line is linked to a planned joint investment of €2.5 billion and is intended, among other things, to provide companies, start-ups, universities, and design and systems providers with access to advanced technology and prototyping.

In February 2026, imec opened an additional 2,000 square meters of cleanroom space as part of this expansion. The project is explicitly organized at the European level and includes partner sites, among them Fraunhofer in Germany. Germany is also one of the participating funding states. Therefore, the notion that national research and increased use of imec are fundamentally alternative approaches does not adequately reflect this interconnectedness.

An economically sensible approach is a division of labor in which specialized, large-scale infrastructure is used in a concentrated manner while simultaneously allowing broad access for research and businesses. Building expensive facilities at numerous locations in parallel can be inefficient. Conversely, cutting-edge infrastructure is of little use if smaller companies cannot practically access it.

The right questions, therefore, are: What skills are actually lacking? Where do internationally relevant strengths already exist? How do young companies gain access to design tools, testing facilities, and manufacturing partners? And which applications justify the considerable development costs in the long term?

A promising semiconductor strategy needs priorities. It doesn't have to represent every stage of the value chain entirely nationally, but rather secure critical competencies and create reliable access to complementary skills.

Research is indispensable, but its funding requires different success metrics

Criticism of lengthy research projects contains a valid point when it focuses on a lack of relevance and unclear use of public funds. However, it would be incorrect to conclude that scientific training is inherently less valuable than direct corporate funding.

Research and entrepreneurial development fulfill different functions. Basic research can generate knowledge whose economic application is initially open. A market-oriented development project, on the other hand, should be able to explain much more precisely which problem is being solved and what subsequent use might look like.

Evaluating both using the same metric would be a mistake. If every research project were judged solely on short-term revenue, long-term opportunities for knowledge would be lost. Conversely, if every application-oriented project were evaluated solely on the basis of scientific publications, economic viability would remain secondary.

The quality of a person or a project cannot be reliably derived from the type of university attended or a single grade. For industrial AI, practical experience, systematic testing, and a good understanding of the application area can be just as important as top-level theoretical knowledge.

The real problem often lies in the transitions. Research findings must be technically stabilized, translated into products, and linked to real customer requirements. This work is neither classic basic research nor routine sales. It requires different skills and often different funding as well.

A balanced policy should therefore protect scientific quality while simultaneously taking knowledge transfer seriously. Joint projects with clear responsibilities, temporary staff exchanges between research and industry, and transparent handling of usage rights can be more important than another organizational funding level.

An AI safety institute must address public risks, not displace existing markets

The German government assigns an analytical and operational function to the German AI Security Institute (AISI). It is not intended to be a regulatory authority, but rather a center of excellence for technology analysis, risk assessment, and strategic consulting. This includes situational awareness and early warning information with regard to society, the economy, and national security.

This delineation of responsibilities is crucial for economic evaluation. A public security mandate differs from the testing of a specific company product. Systemic risks, new attack vectors, or security-relevant developments may require insights that no single private client would fully finance.

There is a justifiable reason for government intervention here. If many stakeholders benefit from improved risk assessment, but none want to bear the entire cost, a public institution can fill a gap in services.

The situation is different for standardized testing services tailored to clearly defined customer needs. In these cases, private-sector testing and consulting services can be beneficial. A government institution should not duplicate these activities without demonstrable added value.

The practical challenge, therefore, lies in a precise division of labor. Public authorities could investigate overarching risks, further develop testing methods, and make the findings accessible. Private providers could then build upon this foundation to offer specific audits, implementation support, and recurring inspections.

It is also important to avoid duplication of effort. If companies have to repeatedly produce nearly identical documentation for different clients and institutions, additional costs arise without a corresponding increase in security. Recognized and transferable results would be more economically valuable than a growing number of separate testing processes.

A security institute is therefore neither automatically necessary nor automatically superfluous. Its usefulness depends on whether it fills a clearly defined gap, whether its results are practically applicable, and whether its tasks remain distinct from existing market offerings.

More computing power is needed, but not yet a business model

The German government aims to at least double the overall data center capacity in Germany by 2030 and to quadruple the capacity for AI and high-performance computing. This addresses a key infrastructure bottleneck. However, additional capacity initially represents a supply and not yet guarantees economic success.

For users, what matters is whether computing power is available at reasonable prices, with suitable tools, and under the required operating conditions. A medium-sized machine manufacturer doesn't necessarily need direct access to a large training system. They may need a reliable, affordable environment for a specialized application.

Training and ongoing operation must also be distinguished. Developing a large model can require enormous computing resources. In contrast, a narrowly defined business application can sometimes be run with a smaller model. Which architecture is economically viable depends on the task, the workload, and the quality requirements.

Therefore, capacity planning should be linked to a realistic demand analysis. Factors to consider include power supply, grid connections, cooling, available skilled personnel, and the pace of technological change. A plant can be technically efficient yet still operate inefficiently if its utilization remains too low.

Conversely, it would be equally problematic to wait to invest until every future application is certain. Infrastructure often needs to be built before full demand is met. Therefore, flexible concepts, diverse usage models, and a transparent distribution of investment risk are crucial.

The economic benchmark should not be solely installed capacity. Equally relevant are actual usage, cost per meaningful computing task, availability, and access for smaller businesses. Infrastructure only becomes a competitive advantage when it enables productive activities that would otherwise not take place or would be significantly impaired.

Good AI policy needs fewer symbolic numbers and better impact data

The announced targets provide a basis for political control. However, they carry the risk that easily measurable activities will be mistaken for actual results.

A usage rate initially indicates how widespread a technology is. It doesn't explain how intensively it's used or whether the investment pays off. The number of funded companies describes the reach of a program. It doesn't yet prove that these companies would have fared worse without funding.

This additional effect is crucial. If a company finances an already planned investment solely with public funds, the result may be a mere windfall gain. However, if support enables an economically viable implementation that would otherwise have failed due to a clear bottleneck, it can create genuine added value.

A reliable assessment would therefore need to define the expected changes before a program even begins. For an initial program, this could be the subsequent self-financed use. For industrial applications, the focus might be on throughput times, quality improvements, or a lower error rate. For infrastructure, accessibility and utilization would be key factors.

The target figure of more than ten percent of AI-based economic output also requires a transparent definition. If every AI-supported activity is fully included, the result will differ from considering only the additional value created. Without a clear definition, the metric would be politically impressive but analytically weak.

A good reporting system should therefore combine a few, meaningful key performance indicators (KPIs) with transparent methodologies. This also includes failed projects and completed initiatives. A strategy in which every program continues regardless of the outcome fails to learn from its own experiences.

Political responsibility is demonstrated through implementation competence and clear responsibilities

Blanket criticism of generations is insufficient to explain the problems of AI policy. Technological competence cannot be reliably determined by age. Young decision-makers may argue superficially, while experienced leaders can effectively manage change.

The more important question is which competencies inform political decisions and who is responsible for the results. AI policy requires technical knowledge, an understanding of operational processes, and the ability to prioritize under uncertainty.

The economy should not be treated as a monolithic entity. Model providers, cloud companies, integrators, industrial conglomerates, and small users all have different interests. A program that is attractive to one provider is not necessarily beneficial for the economy as a whole.

Close involvement of companies is therefore necessary, but it does not replace independent assessment. Otherwise, there is a risk of policies that protect existing business models instead of strengthening competition and productivity.

The German government envisions its AI task force continuing to manage the implementation of the measures, meeting at least quarterly at the state secretary level. Whether this results in effective management depends not on the frequency of meetings, but on decision-making powers, binding responsibilities, and the ability to actually remove obstacles.

In this context, responsibility also means asserting priorities despite resistance. Not every good idea can be funded simultaneously. Not every existing institution needs a new AI task. And not every program started deserves to be continued indefinitely.

A viable strategy combines market opening, specialization, and consistent selection

From an economic perspective, Germany's AI policy should combine three different tasks.

First, it must make widespread use easier. This requires understandable offerings, appropriate training, easily accessible operating models, and support for data and integration issues. Smaller companies, in particular, benefit when proven solutions don't have to be developed from scratch every time.

In addition, it must support companies that develop internationally scalable products and fundamental technologies. This task requires larger financial volumes, longer time horizons, and a greater willingness to take technological risks. It must not be confused with piecemeal digitalization funding.

Ultimately, the state must provide public infrastructure that individual companies cannot adequately finance. This includes specific research infrastructures, comprehensive security analyses, and common technical standards.

These tasks require different tools. A small company with a specific automation problem doesn't need a national model strategy. Conversely, a European model developer cannot become internationally competitive simply by receiving a few consulting vouchers. And a security institute shouldn't be judged solely on its short-term software revenues.

The clear perspective, therefore, is that Germany should try less to organize every activity under a common AI label and differentiate more clearly between market development, technology development, and public tasks. Existing high-performing collaborations deserve support, provided they remain open enough to other companies and deliver measurable results.

The state must act reliably where it creates demand itself, and exercise restraint where additional structures merely duplicate existing supply. European specialization is often more sensible than attempting to completely replicate every capability nationally.

Whether Germany becomes a strong AI nation by 2030 ultimately depends not on the completeness of a list of measures. What matters is whether companies generate lasting returns from applications, employees work more productively, and public institutions noticeably improve their services. A strategy is successful when it enables these results and eliminates superfluous activities—not when it simply adds more and more.

Xpert.Digital is a data-driven B2B industry hub led by Konrad Wolfenstein . The company acts as an external, quasi-in-house solution for industrial partners, closing operational gaps in marketing, content, and sales – without requiring additional resources on the client side.

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