Marek Kozlowski

Poland’s sovereign-AI thesis is specialization, not a race against US or Chinese frontier labs. Marek Kozlowski wants Polish- or domain-adapted models that are “orders of magnitude smaller” yet match models 10 times larger inside a defined language, culture, or workflow. The payoff is local control, lower inference and deployment cost, on-premise operation, and retained technical capability even if foreign models become unavailable or legally unusable.

Frontier-model support for smaller languages may deteriorate as vendors optimize for core workloads. Kozlowski says some Claude and GPT releases have been flat or worse on Polish linguistic and cultural evaluations as developers emphasize coding and other priority markets. For enterprises building deep integrations, that creates “a huge risk”: a provider can change its target objective, degrading Polish performance and forcing a rollback or vendor migration.

Data scarcity and EU regulation define Poland’s structural disadvantage more than parameter count does. Kozlowski estimates that 90% or more of frontier training data is English or Chinese, while Polish can represent about 1% or less; PLLuM retained roughly 200 billion Polish tokens after deduplication and filtering, versus at least 1 trillion tokens he says an 8-billion-parameter model needs for stable training from random weights. European rules can make the gap worse because compliance constraints “can eliminate 80% of the data” from a prospective training corpus.

PLLuM treats human-created post-training data as its differentiating asset. Its internal tools support dozens or hundreds of annotators producing and editing instructions and preferences, while synthetic material is human-checked because “linguistically poor” instructions can degrade generation quality. The project has also published an almost 100-page recipe, samples on Hugging Face, and the principle that “open source is not only about open weights.”

The strongest near-term economics are in narrow, on-premise models serving 10 or 20 workflows—not general assistants serving thousands. Kozlowski says roughly 1,000 task-specific instructions can be enough, with more preferable, to fine-tune a smaller model to equal or sometimes exceed a giant cloud model used zero- or few-shot. Once a buyer prices 16 GPUs, energy, privacy, and operational control, “you always go through the downscaling.”

PLLuM is public infrastructure rather than a conventional venture-scale market-share play. Funded by Poland’s Ministry of Digital Affairs through a consortium that grew from six to eight institutes and universities, it prioritizes legal compliance, transparency, security, local deployment, and assistants for citizens and municipal offices over customer counts or immediate ROI. Sovereignty here means retaining “the competency and possibility” to build, even if Poland’s model is somewhat worse than the global leader.

Enterprise domain adaptation has a high data threshold that sharply limits the addressable customer base. PLLuM demonstrated continued pre-training for PKO, described as Central and Eastern Europe’s largest bank, but Kozlowski estimates a useful adaptation needs about 10 billion cleaned tokens—perhaps 30–40 billion before filtering. His blunt conclusion, “It’s not so easy to get 10 billion tokens,” makes data inventory, permissions, and curation prerequisites rather than implementation details.

The strategy ultimately rests on a disputed view of frontier progress and its cost curve. Kozlowski sees GPT-5 versus GPT-4 as evidence that improvement is becoming “horizontal,” while Nathan Labenz pushes back that exponentially larger runs—say $10 billion versus $1 billion—might still deliver major jumps. Kozlowski’s answer is demand-led: define the business task and benchmark first, because most deployments he sees do not require frontier reasoning at all.

🔗 Original source & video: Sovereign AI in Poland: Language Adaptation, Local Control & Cost Advantages with Marek Kozlowski