Popular benchmarks like MMLU are inadequate for evaluating sovereign AI models. They primarily test multiple-choice knowledge extraction but miss a model's ability to generate culturally nuanced, fluent, and appropriate long-form text. This necessitates creating new, culturally specific evaluation tools.
Poland's AI lab discovered that safety and security measures implemented in models primarily trained and secured for English are much easier to circumvent using Polish prompts. This highlights a critical vulnerability in global AI models and necessitates local, language-specific safety training and red-teaming to create robust safeguards.
Customizing a base model with proprietary data is only effective if a company possesses a massive corpus. At least 10 billion high-quality tokens are needed *after* aggressive deduplication and filtering. This high threshold means the strategy is only viable for the largest corporations, a much higher bar than most businesses realize.
The "agentic revolution" will be powered by small, specialized models. Businesses and public sector agencies don't need a cloud-based AI that can do 1,000 tasks; they need an on-premise model fine-tuned for 10-20 specific use cases, driven by cost, privacy, and control requirements.
While US AI labs debate abstract "constitutions" to define model values, Poland's AI project is preoccupied with a more immediate problem: navigating strict data usage regulations. These legal frameworks act as a de facto set of constraints, making an explicit "Polish AI constitution" a lower priority for now.
Poland's AI lead observes that frontier models like Anthropic's Claude are degrading in their Polish language and cultural abilities. As developers focus on lucrative use cases like coding, they trade off performance in less common languages, creating a major reliability risk for businesses in non-Anglophone regions who depend on these APIs.
Unlike US firms performing massive web scrapes, European AI projects are constrained by the AI Act and authorship rights. This forces them to prioritize curated, "organic" datasets from sources like libraries and publishers. This difficult curation process becomes a competitive advantage, leading to higher-quality linguistic models.
A core motivation for Poland's national AI initiative is to develop a domestic workforce skilled in building large language models. This "competency gap" is seen as a strategic vulnerability. Having the ability to build their own models, even if slightly inferior, is a crucial hedge against being cut off from foreign technology or facing unfavorable licensing changes.