The declaration and the computing question
Artificial intelligence is often presented as software floating above geography. The current Central and Eastern European policy map suggests the opposite. At the 2026 CEE AI Summit in Prague, the Czech Republic, Slovakia, Poland, Croatia, Hungary, Lithuania, Latvia, Slovenia and Romania adopted a declaration on closer cooperation, including work around AI gigafactories. The Czech Ministry of Industry and Trade described a gathering of more than 250 representatives from government, business and research.
Declarations do not guarantee implementation, but they reveal what governments believe is at stake. Compute, data, energy, talent and regulation are no longer separate technical subjects. Together they form the environment in which a company decides whether it can train, test and deploy an AI system. The region’s ambition is therefore architectural: to assemble enough connected capacity that promising work does not automatically move elsewhere.
The word factory gives computing infrastructure a recognisable industrial shape. It suggests organised capacity that can be shared, maintained and put to work. The metaphor is useful only if its limits remain visible. An AI Factory does not simply produce finished applications for every business that approaches it. Computing, expertise and access arrangements still have to meet a project with a defined purpose. The cultural story concerns the institution around the machine as much as the machine itself.
Infrastructure beyond the model
The European Union’s AI Factory programme gives this ambition a physical footprint. The European Commission lists facilities in countries including Poland, Czechia, Lithuania, Romania, Austria, Bulgaria and Slovenia, alongside antenna sites in Hungary, Latvia and Slovakia. These are not factories in the traditional sense of an assembly line. They are ecosystems built around AI-optimised supercomputing, technical expertise and access for startups, researchers and industry. The choice of metaphor is still useful.
A factory concentrates equipment, labour and process so that an idea can become repeatable output. In AI, the equivalent includes computing time, curated data, engineering support and a route from experiment to application. For smaller companies, access to that infrastructure may matter more than a slogan about innovation. The decisive question is whether the system is usable without requiring the resources of a large multinational.
Reading the relationships
| Element | What is visible | What it helps explain |
|---|---|---|
| Declaration | Countries establish a direction for cooperation. | A political framework can support coordination without proving that every practical service is already available. |
| Infrastructure | The programme organises computing and associated support. | Usability depends on access conditions and expertise as well as the scale of the machines. |
| Project | An organisation brings a particular technical problem. | A credible result requires suitable data, clear aims and evaluation rather than computing power alone. |
Who can reach the new capacity
Investment decisions add urgency. A recent Financial Times report described companies assessing countries for “AI openness” through talent, infrastructure and regulatory conditions. The term comes from business rather than public policy, and it should not be treated as a neutral measure. Companies may prefer fewer constraints, while societies have legitimate reasons to demand privacy, safety and accountability. Still, the framework highlights a practical competition. A country can have strong engineers and research institutions yet appear difficult if grid capacity, permitting, data access or regulatory guidance is uncertain.
Central Europe’s response cannot be a race to remove every safeguard. Its stronger proposition may be clarity: shared standards, reliable infrastructure and institutions capable of helping smaller organisations navigate the rules. Openness becomes credible when it means accessible capacity and predictable responsibility, not simply an absence of limits.
Consider a small organisation with a useful idea but limited technical experience. More computing power would not automatically tell it which data it may use, what a successful result would look like or how to evaluate an unreliable output. Those questions need explanation and judgement. This hypothetical case shows why skills and support belong beside infrastructure in the discussion. It also helps distinguish a political agreement from a working service. A declaration can establish a direction; the practical route into that direction depends on criteria, guidance and the people able to make them understandable.
Three details worth noticing
- Separate a declaration from an operational service. Shared political intent and a usable route into computing capacity are different forms of progress.
- Ask about skills, support and project criteria alongside hardware. A machine’s capacity does not define a participant’s ability to use it well.
- Keep evaluation inside the project. An organisation needs to understand the intended result and its limitations before greater computing power becomes useful.
Rules alongside machines
Regional cooperation is attractive because no single medium-sized country can easily reproduce the entire AI supply chain. One may host a supercomputer, another a specialised research group, while firms and datasets are distributed across several markets. Coordination can reduce duplication and create a larger field for testing applications in manufacturing, health, language technology or public administration. Yet cooperation must survive the pull of national prestige.
A gigafactory is a visible prize; shared training programmes, interoperable data spaces and cross-border support services are less photogenic. The latter may determine whether the infrastructure produces broad value. There is also a language question. Models designed primarily around English can perform unevenly in smaller European languages, making regional datasets and evaluation essential. Building that capacity is not cultural decoration around the technology. It shapes who can use the systems safely and whose institutions remain legible in a machine-mediated environment.
A factory without a familiar production line
The contextual software and fabrication photographs illustrate a broader culture of technical work. They are not presented as documentation of AI Factory facilities or participating organisations. That boundary keeps the article from turning a familiar screen into evidence of a specific public investment. The important institutional questions are not always easy to photograph. Who decides which project receives support? How does a participant understand the conditions?
Which parts of the work remain their own responsibility? A public programme becomes credible through answers that a prospective user can follow, not merely through the scale of its equipment.
For Central and Eastern Europe, shared capacity may offer a way to connect technical expertise across different starting points. That possibility should be read as an ambition rather than proof that regional inequalities have already been solved. A research team, an established business and a newer company may need different routes into the same infrastructure. The strongest model would recognise those differences while giving each a clear account of what is available.
The Prague Declaration places cooperation inside the public conversation; the AI Factory programme gives the conversation a more concrete institutional setting. Their relationship will matter most at the point where an organisation arrives with an actual problem. If the programme can help turn that problem into a careful, testable project, computing power becomes something more useful than a national announcement: it becomes capacity that people can learn to use responsibly.
A regional institution for difficult work
The Prague Declaration and the AI Factory network mark a change in scale, but the outcome is still open. Infrastructure can attract talent and companies; it can also become an expensive symbol if access is slow, energy assumptions are weak or support stops at the data-centre door. The region’s opportunity lies in treating AI as a connected public and industrial system rather than a sequence of model announcements.
Compute must meet education, regulation must meet implementation and national programmes must be able to speak to one another. If that happens, Central Europe may offer something more durable than a cheaper location for external investment. It could build institutions capable of setting research agendas and supporting companies on their own terms. The race is therefore not only to possess powerful machines. It is to design the conditions under which those machines become useful, accountable and meaningfully available to the economies around them.