OpenAI expands in Dublin: What the new Applied AI Lab means for European companies

From AI demo to productive use: Where Europe's competitive advantage arises

European companies not only lack high-performance AI models – often, they lack the ability to reliably and cost-effectively integrate them into their daily operations. Between a convincing demo and an application that functions sustainably with real data, existing systems, and clearly defined responsibilities lies a demanding implementation process. This is precisely where so-called Applied AI Labs come in: they are designed to translate technological possibilities into concrete, controllable business processes. OpenAI's planned expansion in Dublin is now bringing this approach more sharply into focus. However, new jobs and a prominent location are not yet proof of productivity gains. The crucial factor is whether such teams create measurable benefits, manage risks, and simultaneously help companies build their own expertise and sustainable implementation capabilities. Because Europe's AI competitiveness will not be determined solely by the models – but by what actually works better in practice.

Billions spent on models, but too little impact in practice: Europe's AI problem begins after the demo

European companies need more than just powerful artificial intelligence. Above all, they need the ability to reliably, economically, and controllably integrate existing AI into their business processes. Between an impressive demonstration system and an application that functions daily under real-world conditions lies a challenging integration task. Applied AI Labs position themselves precisely at this interface: as specialized units that translate technological possibilities into operational performance.

The planned establishment of such a lab at OpenAI's European headquarters in Dublin makes this development particularly evident. The project is part of a larger site expansion. On October 12, 2026, 200 additional positions were announced, supplementing the 250 positions already committed in July. This brings the total commitment to at least 450 new jobs in Ireland. This figure explicitly refers to the entire expansion and not solely to the Applied AI Lab. At the time of the announcement, OpenAI employed more than 120 people in Ireland. The move to the new EU headquarters in the Tropical Fruit Warehouse is scheduled for December 2026; a separate, firm opening date for the lab was not specified.

From an economic perspective, the interesting aspect is not so much the label of a new laboratory, but rather the underlying division of labor. Forward-deployed engineers are meant to develop and implement applications directly with customers. Solutions engineers support the technical implementation, while privacy engineers incorporate data protection requirements into the system design. This brings to the forefront a question that often gets lost in the public AI debate behind model comparisons and investment sums: Who assumes the concrete responsibility for ensuring that AI actually works within the company?

The rationale, therefore, is that Applied AI Labs can become a crucial infrastructure for AI dissemination. However, their economic value doesn't stem from their name, their affiliation with a well-known model provider, or the number of announced jobs. It only arises when they enable measurable improvements, manage risks, and build enduring capabilities for customers.

The real bottleneck lies between technical capability and operational change

An AI model can analyze texts, summarize information, generate program code, or formulate recommendations. However, this does not automatically mean that a company will make money from its use. Economic benefit only arises when a technical capability is combined with a specific task, suitable data, clear responsibilities, and a functioning operational process.

A demonstrator can, for example, show how a customer inquiry can be answered automatically. In production use, however, the same application must recognize which contract terms apply, which information is up-to-date, and whether the requesting customer is even authorized to receive certain details. It must handle incomplete input, escalate in cases of uncertainty, and document its work transparently. Furthermore, it must be integrated into the systems in which employees actually work.

These requirements are not mere afterthoughts. They determine whether an application saves time or creates additional work. A convincing answer based on outdated terms and conditions may be worthless in sales. A quick summary that omits crucial exceptions can even cause problems in contract processing.

The economic challenge, therefore, lies in bringing together several types of knowledge. Technical knowledge about models and software must be combined with expert knowledge about products, customers, machines, or supply chains. Organizational knowledge is also crucial: Who is authorized to decide, what exceptions are acceptable, and which errors are tolerable?

Applied AI Labs addresses precisely this translation challenge. The Dublin-based project aims to support European companies in transitioning from pilot projects to production applications. Direct collaboration with customers is not merely a secondary sales tool, but rather the core of the announced approach. Whether this results in a viable business model depends on how well customer-specific work can be combined with reusable technical building blocks.

AI usage in Europe is growing, but widespread adoption is not yet proof of productivity

The situation is contradictory. On the one hand, AI is spreading significantly in European companies. On the other hand, the use of a technology says little about how deeply it is integrated into the value chain.

In 2025, 20.0 percent of the surveyed EU companies with at least ten employees used AI technologies. The previous year, this figure was 13.5 percent. The increase of 6.5 percentage points demonstrates significant momentum. However, it does not mean that one-fifth of European companies are already fully reliant on AI. The statistics record the use of at least one of the AI ​​technologies examined and refer to specific economic sectors; micro-enterprises with fewer than ten employees are not included.

The difference based on company size is particularly revealing. By 2025, around 17 percent of small businesses would be using AI, compared to about 30 percent of medium-sized businesses and around 55 percent of large businesses. Large companies are thus significantly more likely to have such applications. While these figures demonstrate a size gap, they do not, in themselves, fully explain its causes.

Economically, this gap can be linked to the fixed costs of implementation. Data must be made accessible, systems connected, employees trained, and responsibilities clarified. A large corporation can spread such expenses across many users, locations, or transactions. A smaller company bears similar basic tasks with a significantly lower deployment volume.

This creates a potentially important market for specialized implementation partners. If Applied AI Labs efficiently solve recurring integration problems, they can reduce entry costs. However, if expensive individual projects are built for each client, access remains primarily reserved for financially strong companies. Therefore, the crucial factor is not just whether external support is offered, but whether it actually reduces the overall costs of a viable implementation.

Dublin represents a strategic shift from model access to customer integration

The expansion in Dublin can be seen as an attempt to more closely link OpenAI's European presence with concrete application development. New positions are being created not only in engineering, but also in sales and user support. The lab is thus part of a broader technical and commercial site development strategy.

For a model provider, this proximity to the customer is attractive for several reasons. First, real-world obstacles to implementation can be identified immediately. Second, it leads to a better understanding of which functions companies need and which requirements prevent productive use. Third, the combination of model access and implementation support can deepen the customer relationship.

These interests can align with customer goals. A company benefits from solving technical problems more quickly and not having to develop every integration pattern itself. At the same time, a potential conflict of objectives arises: The provider wants to commercially distribute its technology, while the customer needs the best and most sustainable solution for its process in the long term. These two goals are not necessarily identical.

A lab tied to a specific model provider will therefore be judged primarily on its integration capabilities and the transparency of its architectural decisions. Which components are interchangeable? Which data and test sets remain with the customer? What ongoing obligations arise? And does an application remain economically viable if prices, models, or terms of use change?

The location decision alone does not answer these questions. However, it shows that the competition for corporate clients is increasingly shifting to an additional level: Not only the model, but also the ability to integrate it into operations is becoming a strategic offering.

What the Irish business figures show and what they do not show

The report of November 6, 2026, supplemented the expansion announcement already published in October with information from OpenAI Ireland's financial figures. The new aspect was not the initial announcement of the lab, but rather a more detailed look at the financing and development of the Irish company.

For 2025, a profit after taxes of approximately €269,000 was reported. The number of employees increased year-on-year from 17 to 40. In addition, the company received €65 million in further intra-group loans in 2025, bringing the total amount of loans to €166 million. Cash and cash equivalents rose from approximately €891,000 to €164 million. These figures refer to the Irish company and should not be interpreted as a separate budget for the Applied AI Lab.

This distinction is crucial for a sound economic assessment. A high cash balance initially indicates financial resources. It is neither proof of successful AI projects nor of productivity gains already realized with customers. Likewise, no reliable conclusions about the profitability of the European end-customer business can be drawn from the reported profit.

The Irish company generates its revenue through an intra-group cost-plus agreement. Its condensed financial statements do not include revenue figures for 2025. Therefore, the published profit primarily reflects the company's function within the group structure and not necessarily the economic success of individual products or European customer relationships.

The reported expenditure of €8.27 million for share-based compensation also requires a more nuanced analysis. Of this amount, €7.19 million was passed on to other group companies. Such figures illustrate the extent to which employee compensation and financing can be organized across group boundaries. However, they do not allow for a reliable calculation of average individual salaries.

The figures thus support the assessment of a financially sound site expansion. Whether this expansion will be economically successful can only be judged based on later results.

Applied AI Lab refers to a function, not a protected success model

The term "Applied AI Lab" does not refer to a single institution or a uniformly defined organizational structure. OpenAI uses it for its own application-oriented activities. Unframe also positions itself as an Applied AI Lab, but associates it with a separate offering for implementing enterprise-related AI solutions.

The commonality lies in the stated function: to translate advances in fundamental AI capabilities into reliable business applications. The organizational structures, however, can differ significantly. A lab within a model provider has different incentives and resources than an independent integration provider or an internal unit of an industrial conglomerate.

Unframe describes its approach as a combination of a technical platform, customer-specific implementation, and ongoing support. Possible deployment models mentioned include the customer's cloud, on-premises infrastructure, and a fully managed service. This is a relevant description of the service offering, but not yet independent proof of its quality or cost-effectiveness.

For corporate clients, the label is therefore of secondary importance. More important are concrete performance characteristics: the depth of process knowledge, the quality of integration, the transferability of results, and responsibility in ongoing operations.

A lab that primarily builds demonstrators fulfills a different function than a unit that monitors applications, analyzes errors, and is responsible for updates. Similarly, a consulting-oriented offering differs from a continuously operating system. Those who conflate these categories often end up comparing prices and performance promises that relate to different tasks.

The economically relevant question is therefore not whether a company needs an Applied AI Lab. It is which implementation capabilities are lacking and which internal or external structure can best close this gap.

Customer-focused engineering can close the most expensive knowledge gap

The approach of forward-deployed engineers is particularly plausible where operational requirements are not fully documented. A significant portion of the relevant knowledge only emerges in daily work: in special cases, informal discussions, established approvals, and rules of thumb learned through experience.

In a logistics company, for example, a delayed delivery can have several causes requiring different responses. Missing goods, an incorrect timestamp, and a blocked loading dock might technically be similar exception reports, but operationally they represent completely different problems. An AI application that only classifies text does not automatically capture these differences.

Direct collaboration between specialist departments and engineering can reveal such connections. Developers identify which data is actually needed. Specialists see which parts of a task can be automated and where human judgment remains necessary. Ideally, this leads to changes not only in the software but also in the process.

However, this approach is labor-intensive. Highly qualified teams that delve deeply into individual client organizations cannot manage an unlimited number of projects simultaneously. This creates a business tension between individualization and scalability.

A viable model must therefore differentiate between customer-specific process knowledge and reusable components. Access management, logging, testing procedures, and certain data connections can be standardized. Business decision rules and exceptions often require individual configuration.

The key to productivity gains lies not in building every customer solution from scratch. It lies in precisely identifying the non-standardizable components and systematically reducing the remaining effort. If this separation fails, the Applied AI Lab risks becoming merely a new label for perpetually expensive project work.

An AI application only earns its place through a complete cost-benefit analysis

The profitability of an AI application cannot be derived solely from its technical functionality. The crucial factor is the difference between its realized benefits and all additional costs.

A simplified, explicitly hypothetical example illustrates the connection. A company processes 100,000 document-related transactions annually. An application saves an average of six minutes per transaction. This theoretically results in 10,000 hours of freed-up working time. Assuming total costs of €50 per hour, this corresponds to a capacity value of €500,000.

This amount does not yet represent a savings in the annual financial statements. It initially describes a theoretical potential. Employees continue to be paid, and the saved minutes cannot be fully integrated into every workflow. A financial advantage arises, for example, when overtime is eliminated, additional orders are processed without a corresponding increase in staff, or freed-up capacity is used for other economically relevant tasks.

Assuming total ongoing costs of €180,000 and one-time implementation costs of €220,000 in this example, full realization of the capacity's value would result in an annual surplus of €320,000 before any other effects. However, if only 40 percent of the calculated benefit were actually realized, only €20,000 would remain annually after ongoing costs. The one-time implementation would then be recouped very slowly.

The total costs include more than just model calls or licenses. They also encompass data preparation, interfaces, testing, training, monitoring, human review, and subsequent adjustments. Costs associated with errors and downtime are equally important.

A reputable applied AI lab shouldn't create this calculation only after the application is finished. It should use it as a design basis from the very beginning. Otherwise, technical feasibility will be confused with economic viability.

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Productivity arises from better division of labor, not from maximum automation

Empirical studies show that generative AI can improve job performance under certain conditions. They also show how strongly the effect depends on the specific field of application and the experience level of the employees.

A study of 5,179 customer support employees found an average productivity increase of 14 percent, measured by successfully resolved requests per hour. For inexperienced and lower-skilled employees, the improvement was 34 percent. For highly experienced and high-performing employees, the additional effect was minimal. These results are from a specific application and do not represent a general return on investment forecast for AI projects.

The economically interesting interpretation lies in the distribution of knowledge. An assistance system can help employees apply proven procedures more quickly. The benefit then arises not only from saved typing time, but also from shorter learning curves and easier access to relevant experience.

This has an important consequence for application design: Maximum autonomy is not automatically the optimal goal. In many processes, a well-integrated assistance system can be more economical than a largely autonomous application that requires extensive monitoring.

A system that reliably provides the relevant information and formulates a verifiable proposal can make a greater contribution than an autonomous process whose errors are only detected late in the process. The decisive factor is the quality of the entire division of labor between humans and machines.

Applied AI labs should therefore not only investigate which tasks can be technically automated. They must also clarify where support generates the greatest marginal benefit, which decisions should remain with humans, and how employees can maintain their expertise despite increasing automation.

Data protection and security belong in the architecture of value creation

The planned privacy engineering roles in Dublin are economically relevant because they address data protection as a design challenge within technical development. This can help to consider requirements early on, instead of having to fundamentally redesign an already developed application. Whether this effect is achieved in any given case depends on the actual collaboration between engineering, specialist departments, and data protection officers.

At its core, this is about practical questions: What data does the application really need? Which users are allowed to see which information? How are different customer or project areas separated? What is stored, what is logged, and when is it deleted?

A knowledge application can provide technically correct answers and still be unsuitable if it grants employees access to information to which they lack authorization. Similarly, a good text generator is insufficient if confidential contract data is processed without appropriate organizational and technical safeguards.

As the range of possible actions increases, so do the requirements. A system that compiles documents has a different risk profile than an application that triggers orders, modifies master data, or sends messages to customers. The greater the intervention rights, the more important approval levels, restricted permissions, and traceable action logs become.

From a business perspective, security is therefore not just a cost factor. It can be a prerequisite for an application to be used in value-creating processes at all. At the same time, it would be wrong to implement every conceivable control measure regardless of the risk. Overly complex procedures can unnecessarily increase the cost of simple applications.

The appropriate target parameter is a suitable level of control: robust enough to withstand potential damage, but not more complex than necessary. A high-performing Applied AI Lab must be able to achieve this balance in collaboration with the client.

Industry and logistics need reliable process systems, not impressive user interfaces

In industry and logistics, the gap between convincing demonstration and economically viable application is particularly pronounced. Here, language processing and knowledge work meet physical processes, technical conditions, and binding performance commitments.

An industrial assistant could access maintenance documentation, structure fault reports, or support service technicians in finding relevant information. However, its usefulness depends on its ability to identify the correct machine variant, consider the current documentation status, and respect safety-related boundaries. A plausible but incorrect recommendation can have more serious consequences than a delayed response.

Logistics often faces additional challenges due to time constraints and data inconsistencies. Orders, transport notifications, inventory levels, and customer communications may reside in disparate systems. Therefore, an application for handling delivery discrepancies must offer more than just a convincing explanation. It must consolidate information, identify uncertainties, and support appropriate processing steps.

This is precisely where application-oriented engineering can be highly valuable. The bottleneck often isn't that an AI can't formulate a message. It's that it's not clearly defined which system contains the valid status and who is authorized to act in case of a deviation.

Therefore, clearly defined tasks with a high volume of repetition are often economically attractive. These can include pre-structuring service cases, searching technical documentation, or supporting recurring customer communication. Whether such an application is profitable must be assessed based on the specific process.

The crucial dividing line is not between modern and outdated technologies. It lies between an application that supports responsibility and an application that merely generates additional output on a screen.

Cloud marketplaces facilitate access, but do not replace integration work

On October 27, 2026, Unframe announced its availability on the AWS, Google Cloud, and Microsoft marketplaces. The offering is designed to allow businesses to access these services through existing cloud accounts and established commercial structures. Depending on the agreement, previously committed cloud spending can also be taken into account.

Economically, this addresses a different hurdle than the actual development of an AI application. Companies must not only technically evaluate new technologies but also integrate them from a commercial and organizational perspective. An existing sales channel can simplify this coordination.

However, this does not automatically result in cost savings or a guarantee of technical quality. An offering obtained through a well-known marketplace must still be evaluated with regard to its data processing, architecture, operating costs, and suitability for the specific process. Furthermore, using existing budgets does not make an application free. It primarily changes which budget and channel are used for payment.

For providers, such a presence can facilitate market entry. For customers, it can reduce transaction costs. However, the productive benefits still only arise in the application itself.

Particularly careful should be paid to performance promises that promise very rapid delivery or measurable benefits in the short term. Unframe describes such capabilities in its corporate communications. While such statements characterize the provider's positioning, they do not constitute general proof of performance for different customer situations without independent verification.

The sensible distinction is therefore: Easy access can facilitate implementation. It cannot replace professional, technical, and economic review.

Reuse determines whether projects become a scalable business model

An Applied AI Lab faces a difficult economic balancing act. Corporate clients expect solutions that take their specific needs into account. At the same time, providers must deliver their services efficiently and with consistent quality.

If every project requires new data connections, new control mechanisms, and new testing procedures, costs increase with almost every additional customer. Scaling then becomes primarily a matter of hiring additional staff. A more product-oriented approach, on the other hand, attempts to transform recurring tasks into common building blocks.

Unframe describes a customer case in which the use of Cushman & Wakefield expanded from a single strategic application to 18 use cases within approximately one year. This is a concrete vendor statement regarding the adoption rate within a client organization. It does not, in itself, demonstrate the magnitude of the economic benefit or the profitability of each individual use case.

The underlying mechanism is nevertheless economically sound. If a company has already established data access, authorizations, operational monitoring, and organizational responsibilities, further applications can build upon parts of this foundation. The first use case can thus finance infrastructure that will later be used multiple times.

However, reuse is not free. Common platforms must be maintained, documented, and adapted to new requirements. Furthermore, excessive standardization can lead to business processes being forced into unsuitable technical templates.

The viable model lies between two extremes: neither completely individualized project work nor a rigid, one-size-fits-all solution for all companies. Crucially, a common technical foundation with clearly defined, customer-specific components is essential. Only this combination offers the opportunity for both the supplier and the customer to benefit from increasing experience.

Singapore shows how AI implementation can become a location strategy

The developments in Dublin are not isolated. As early as May 20, 2026, a collaboration with OpenAI, including an Applied AI Lab, was presented in Singapore. The timing is important: an update to the relevant website in November does not make the initiative published in May a new announcement in November.

The partnership includes a commitment of more than 300 million Singapore dollars for the AI ​​ecosystem. This amount refers to the broader initiative and should not be interpreted as the lab's entire budget. The lab plans to expand its team of forward-deployed engineers and technical specialists to more than 200 positions in the coming years.

The approach combines the introduction of applications with training and broader access to AI skills. Public services, finance, healthcare, and digital infrastructure are mentioned as key areas of focus. Programs for the further training of software developers, as well as offerings for educational institutions, startups, and small businesses, are also included.

From an economic perspective, the combination of various complementary investments is interesting. High-performing models alone are not enough. Likewise, further training without suitable applications is insufficient. Only when technology, skilled workers, institutional support, and real demand come together can broader implementation capacity develop.

This doesn't provide a simple blueprint for Europe. Corporate structures, markets, and institutional responsibilities differ. However, the comparison reveals that Applied AI Labs can be more than just individual development departments. They can act as hubs where technical implementation, training, and regional specialization converge.

Whether this results in lasting local added value depends, in turn, on how much knowledge is built up locally and how widely it becomes available.

Europe's sovereignty depends on its ability to switch countries and its own process knowledge

Building additional implementation capacity in Europe is fundamentally an opportunity. It can help companies to leverage technological capabilities more quickly and gain their own experience. However, this does not automatically lead to technological independence.

A company can operate an application in Europe and still be heavily dependent on specific models, proprietary interfaces, or external expertise. Conversely, it can leverage international technologies and still retain significant control, provided that data, decision rules, and operational knowledge remain sufficiently accessible.

Therefore, a practical definition of sovereignty is economically sensible. The crucial factor is whether a company can modify, test, and, if necessary, switch to other components for its application without incurring disproportionately high costs or operational risks.

This includes transparent architectural decisions, documented interfaces, transferable data sets, and in-house testing procedures. Equally important is whether employees understand the application's limitations. An organization that can only have its AI processes explained by an external partner may have little real autonomy, despite formal ownership rights.

Complete interchangeability, however, is not a cost-effective goal either. Additional abstraction layers, parallel models, and multiple operating paths increase effort and complexity. Therefore, not every small application requires a complex multi-vendor architecture.

The economically sound solution is based on the strategic importance of the process. The greater the dependence on a single application and the greater the potential damage from a failure, the more valuable documented alternatives and controllable transition paths become.

A good Applied AI Lab should disclose this trade-off. It should not only accelerate the launch of an application, but also consider its future adaptability.

For medium-sized businesses, a limited entry point with tangible benefits is crucial

Small and medium-sized enterprises (SMEs) in particular face a dual challenge. They often possess valuable expertise and clearly identifiable operational problems, but have limited staff resources available for extensive transformation programs.

The most sensible starting point, therefore, often lies not in the broadest possible AI platform, but in a manageable process with a clearly measurable starting point. Suitable tasks are those whose volume, processing time, and quality requirements are known. Equally important is that the necessary data can be made available with reasonable effort.

For example, a company needs to know whether it is implementing an application to save time, improve quality, or handle additional demand. These goals can be related, but they are not identical. Quality improvement can be economically valuable even if processing time is barely reduced. Conversely, time savings can be ineffective if they cannot be leveraged organizationally.

External implementation partners should therefore not start with as many use cases as possible. First, a well-founded case is needed to test the technology, collaboration, and operational capability.

Knowledge transfer is also part of the economic outcome. After the project, the company should have a better understanding of existing data problems, necessary controls, and what further applications would be useful based on the resulting data.

Ongoing external support is by no means ruled out. However, it should be a consciously chosen division of labor and not arise from a lack of transparency. For medium-sized businesses, a partner is particularly valuable if they reduce complexity without simultaneously creating an intractable dependency.

The measure of success must shift from pilot figures to realized results

The number of developed demonstrators, launched projects, or trained employees can make activities visible. However, it does not yet measure economic success.

A reliable evaluation framework begins with the original process. How long does processing take? What is the error rate? How often do cases require reprocessing? What are the costs per process? Only on this basis can one assess what changes are brought about by an AI application.

It's important to distinguish between different effects. Faster initial processing isn't progress if subsequent corrections take more time. A higher number of automatically completed processes can be problematic if complaints or incorrect decisions increase. Conversely, a system that automates less but improves the quality of difficult decisions can be valuable.

Development over time is particularly important. Applications must not only function correctly upon launch. Data sources, products, responsibilities, and technical components change. Therefore, ongoing monitoring and retesting are essential for efficient operation.

This creates a demanding performance benchmark for Applied AI Labs. They should not be evaluated solely for providing a function, but for their contribution to a stable, transparent, and economically viable process.

However, this also requires responsibility on the customer's side. Without clear process owners, available specialists, and decisions on how to utilize the resources gained, even good technical implementation can only have a limited impact. Therefore, AI success cannot be completely outsourced to a single provider, nor is it solely the responsibility of the IT department.

The competition will be decided where technology transitions into altered value creation

The expansion of OpenAI in Dublin, Unframe 's application-oriented positioning, and the broader initiative in Singapore all point to the same economic connection: access to powerful AI must be complemented by concrete implementation capacity. While the projects differ in organization, scope, and focus, they share the ambition of transforming technological possibilities into productively usable systems.

This presents an opportunity for European companies, but it is no guarantee of success. Additional engineering capacity can solve integration problems, support qualification, and shorten implementation times. However, it can also create new costs and dependencies if objectives remain unclear or long-term operations are not adequately considered.

The decisive economic factor is therefore implementation competence: the ability to select suitable tasks, prepare data and processes, reliably introduce systems and actually implement improvements.

Applied AI labs can complement and accelerate this capability. However, they do not replace business prioritization or organizational change. Their value is greatest when they combine external technical experience with internal expertise, creating capabilities that extend beyond a single project.

Europe's AI competitiveness will therefore not depend solely on how many companies have access to AI or how many models are available to them. What matters is how many robust applications emerge that reduce costs, improve quality, enable additional performance, and remain manageable under real-world conditions. The decisive progress begins where a compelling demonstration translates into a reliably improved operational process.

Konrad Wolfenstein

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