The European Union is attempting to establish itself as a developer of advanced artificial intelligence rather than primarily a regulator and consumer of technology produced elsewhere. An Italian-led consortium developing a large open-source AI model across all 24 official EU languages will provide an important test of whether Europe’s supercomputing infrastructure can support globally competitive systems.

Europe’s efforts to establish greater control over artificial intelligence technology are entering a new phase as the EUROPA consortium, led by Italian company Domyn, advances plans to develop one of the continent’s largest open-source AI models.

The initiative, supported by the European Commission and European high-performance computing infrastructure, aims to create an advanced artificial intelligence system capable of competing with models developed by leading American and Chinese companies.

Domyn chief executive Uljan Sharka told the Financial Times on 29 October that Europe possesses the technological resources and industrial expertise required to compete in advanced AI development.

However, he also identified two obstacles: the need for greater investment and the difficulty of persuading European businesses to adopt domestically developed systems.

These challenges extend beyond the development of a single language model.

They concern whether Europe can establish a commercially sustainable AI industry while reducing dependence on technology providers headquartered outside the EU.

EUROPA and the Frontier AI Grand Challenge

The European Commission selected EUROPA in June 2026 as the winner of its Frontier AI Grand Challenge.

The competition, launched in February, was designed to support the development of a European general-purpose AI model with more than 400 billion parameters.

EUROPA’s planned model is expected to contain approximately 500 billion parameters and support all 24 official EU languages.

The consortium brings together Domyn and Germany’s Fraunhofer research organisation, including the Fraunhofer IAIS and IIS institutes.

Its work will rely on the EuroHPC network of European supercomputers.

Under the competition arrangements, the winning consortium can obtain access to as much as 2.5 per cent of EuroHPC’s total computing capacity for one year.

This allocation is intended to address one of the largest barriers to advanced AI development: the enormous computing resources required to train models at this scale.

The final system is intended to be released under an open-source licence, allowing organisations to inspect, adapt and deploy the technology.

Nevertheless, parameter count alone does not demonstrate that a model is competitive.

Its actual performance will depend on training data, architecture, computational efficiency, evaluation results and the ability to perform reliably across different tasks.

A European Model for European Languages

One of EUROPA’s distinguishing objectives is its coverage of all official EU languages.

The commercial AI market has historically been dominated by models developed primarily for English-language applications, although leading international systems increasingly support multiple languages.

For Europe, linguistic capability has particular relevance to public administration, legal services, education and cross-border business.

A model designed specifically for the European market could potentially support specialised applications involving national legislation, administrative procedures and technical terminology.

However, multilingual availability will need to be assessed against measurable performance standards.

A system capable of producing fluent responses in a particular language is not necessarily equally reliable when interpreting specialised legal or scientific information.

The consortium will therefore need to demonstrate that linguistic coverage translates into practical capabilities rather than remaining principally a technical specification.

Europe’s Supercomputers Become Strategic Assets

The EUROPA project forms part of a wider effort to use European publicly supported computing infrastructure for commercial and scientific AI development.

The EuroHPC Joint Undertaking has invested in systems intended to provide advanced computing resources to researchers, businesses and public institutions.

Additional AI-focused infrastructure is being developed through the EU’s network of AI factories.

These facilities are intended to make specialised computing capacity available to organisations that would otherwise face considerable costs when developing or adapting AI systems.

However, infrastructure ownership does not necessarily provide complete technological independence.

European supercomputing projects continue to rely on internationally sourced semiconductor technology and specialised software.

Domyn itself has described a partnership with the American technology company Nvidia in its previous model-development work.

Consequently, European AI sovereignty is better understood as a question of control over important technological capabilities than complete separation from international supply chains.

Domyn’s Smaller Model Provides an Early Example

On 20 October, Domyn announced the initial release of Domyn Edge, a separate AI model containing four billion parameters.

According to the company’s technical announcement, the system was trained from scratch using approximately 13 trillion tokens on Europe’s Leonardo supercomputer.

Domyn has committed to making the model’s weights, training code, intermediate checkpoints and datasets available under open licences.

This approach is intended to improve reproducibility and allow external developers to examine how the model was constructed.

Domyn Edge is substantially smaller than the planned EUROPA system and should not be confused with it.

Nevertheless, the project provides an example of the company’s approach to developing AI models using European computing infrastructure.

Regulation Is Only Part of the Challenge

The EU has already established an extensive regulatory framework for artificial intelligence through the AI Act.

However, legislation governing the development and deployment of AI does not, by itself, create a competitive domestic technology industry.

Europe faces competition from American companies with substantial private investment, established cloud platforms and access to large commercial markets.

Chinese developers have also expanded their capabilities, including in open-weight AI systems.

EUROPA’s commercial prospects will therefore depend on more than technical performance.

European businesses must be willing to integrate the resulting technology into their operations, while developers will require dependable access to computing infrastructure and support services.

There is also the question of whether European institutions themselves will adopt domestically developed AI systems where they meet relevant security and performance requirements.

Without sufficient demand, even a technically successful model could struggle to establish a sustainable ecosystem.

The Test Will Be Commercial as Well as Technical

Domyn has indicated that it aims to release EUROPA during 2027, although public statements have differed over the precise timetable.

The company’s leadership has discussed a June target, while an earlier technical announcement referred to the second half of 2027.

The immediate challenge is completing development and demonstrating that the model can compete on performance, reliability and cost.

The longer-term challenge concerns adoption.

If European businesses and public institutions continue relying predominantly on foreign technology, access to an advanced European alternative may have only a limited effect on the market.

Conversely, a competitive and widely adopted open-source model could offer organisations greater control over deployment, customisation and sensitive information.

EUROPA is therefore more than a publicly supported technology experiment.

It is a test of whether Europe can convert research institutions, computing infrastructure and industrial expertise into AI products capable of competing in the international market.

The outcome will depend not simply on whether Europe can build an advanced model, but on whether organisations consider it sufficiently capable and economical to use.

A European Commission survey has found widespread adoption of artificial intelligence among smaller businesses, but considerable reluctance to allow automated systems to make decisions about employees. The findings expose a growing divide between technological investment and confidence in workplace automation.

Artificial intelligence is becoming established across European businesses, but almost half of companies remain unwilling to trust automated systems when decisions concern their employees, according to a European Commission survey published on 14th October.

The Eurobarometer findings show that 42 per cent of the small and medium-sized enterprises and mid-cap companies surveyed already use artificial intelligence or algorithmic management systems, although most deployment remains limited or experimental. A further 21 per cent intend to introduce such technologies in the future.

Despite this expanding adoption, 49 per cent of respondents said they did not trust artificial intelligence or automated systems to make staff-related decisions. The figures suggest that companies increasingly distinguish between using technology to improve business processes and delegating managerial responsibility to software.

The Commission’s survey of European businesses was conducted between March and April 2026, involving approximately 12,826 telephone interviews with company leaders across the EU’s 27 member states.

Businesses prefer assistance to automation

The survey provides a more detailed picture of workplace AI than conventional statistics measuring whether companies have adopted the technology.

Among the businesses interviewed, customer service and client relations were the most commonly reported areas of application, accounting for 47 per cent. Monitoring employee performance and productivity accounted for 36 per cent, while logistics, planning and staff scheduling represented 30 per cent.

These functions differ substantially in their consequences for workers and managers.

A system that helps organise deliveries or forecasts customer demand may improve efficiency without directly determining employees’ working conditions. Software that evaluates performance, allocates shifts or recommends staffing changes can influence employment decisions and requires a different level of organisational supervision.

The survey found that 61 per cent of companies considered AI useful in supporting decisions. However, human involvement remained central: 90 per cent of respondents reported that people continued to make decisions, while fully automated decisions accounted for only 2 per cent.

The results undermine the assumption that adopting AI necessarily means transferring control of business operations to autonomous systems.

For many companies, the immediate commercial value lies in processing information more rapidly, identifying patterns and reducing administrative workloads rather than removing human management entirely.

Investment costs remain a substantial barrier

Financial and organisational obstacles continue to limit adoption.

Half of the companies surveyed cited high investment costs, while 49 per cent identified legal uncertainty and 48 per cent pointed to insufficient expertise.

These findings are particularly relevant to small businesses, which frequently lack dedicated technology teams, extensive data infrastructure or the resources to evaluate complex software systems independently.

For such businesses, purchasing AI tools is only one component of the investment. Implementation can also require employee training, changes to internal procedures, data-security assessments and continuing technical support.

The findings add a workplace dimension to concerns raised in EU Today’s earlier examination of Europe’s AI productivity gap, which identified investment, computing capacity and organisational restructuring as obstacles to translating adoption into higher productivity.

A company may purchase sophisticated software without necessarily achieving corresponding improvements in performance. Those improvements depend on whether the technology is integrated into everyday operations and whether employees have the skills to use it effectively.

Brussels prepares further workplace legislation

The survey arrives while the European Commission is considering additional rules on algorithmic management through its proposed Quality Jobs Act.

The initiative is intended to address working conditions, workers’ rights and the increasing use of automated systems in managing employees.

A consultation launched in July asked European employers and trade unions to consider transparency in automated decisions, excessive workplace monitoring and the responsibilities of companies using AI.

The second consultation phase ended in September, allowing social partners to propose negotiated arrangements before the Commission determines its next legislative steps.

The broader regulatory framework is already changing. As EU Today explained when the AI Act entered a new enforcement phase, Brussels has begun enforcing certain obligations for general-purpose AI models, while important requirements for high-risk applications, including employment-related systems, follow a later timetable.

Stand-alone high-risk systems are scheduled to face the relevant obligations from December 2027, with another deadline in August 2028 for high-risk systems embedded in regulated products.

The distinction matters for employers planning investments today. Existing data-protection and employment obligations continue to apply, but some of the AI Act’s more specific compliance requirements remain subject to transition periods.

The Commission’s latest survey indicates that businesses are already exercising caution independently of these future obligations. Their reluctance to automate sensitive staff decisions suggests that accountability, reliability and employee confidence will remain important factors in determining how extensively artificial intelligence is deployed.

For Brussels, the challenge is to establish workable rules for that transition without making implementation prohibitively expensive for smaller companies, which account for much of Europe’s employment and commercial activity.

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