📊 Full opportunity report: AMÁLIA · The Three Hard Questions. on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
Portugal’s AMÁLIA, a €5.5 million European Portuguese LLM, is now operational. However, critical questions about its openness, native-language data, and optimization goals remain unanswered, highlighting broader issues in European sovereign AI efforts.
Portugal’s €5.5 million investment in the AMÁLIA large language model has resulted in a functioning, publicly accessible version, marking a significant step in the country’s AI ambitions. However, key structural questions about the model’s openness, native-language data, and strategic goals remain unaddressed, raising broader concerns about the European sovereign-LLM movement.
AMÁLIA is a consortium project involving approximately 60 researchers across Portugal’s leading academic institutions, including NOVA, IST, and IT. The model, which handles text only in its current form, was completed in September 2025 and is available to 450,000 academic users through the FCT’s IAedu platform. It is based on a continuation of the EuroLLM multilingual foundation, with a focus on Portuguese, and has outperformed previous open models on Portuguese benchmarks, though it still trails Qwen 3-8B on some key tests.
The project was publicly announced in December 2024, with the government committing €5.5 million, and the final version is expected in June 2026. The technical approach involves extending an existing multilingual model rather than training from scratch, a strategic choice that influences the model’s capabilities and potential.
AMÁLIA
The three hard
questions.
Portugal spent €5.5M to build a European Portuguese LLM. The base version is operational, the benchmarks beat Qwen 3-8B on most pt-PT tasks. So why are the most important questions still unanswered?
Last month, Duarte O.Carmo published the sharpest public analysis of AMÁLIA — Portugal’s state-funded European Portuguese large language model. He prefaces his critique with the necessary diplomatic apparatus before doing what almost nobody else in the European-sovereign-LLM discourse has been willing to do publicly: asking hard questions about whether the work, as released, actually does what it set out to do. This piece is a structural extension of his analysis. The AMÁLIA case study exposes three hard questions every national LLM effort needs to answer publicly — and the broader European sovereign-LLM movement has been operating without explicit answers to any of them.
Three questions every national LLM effort needs to answer publicly.
Duarte O.Carmo’s framing maps cleanly onto the structural argument. Each question lands specifically in AMÁLIA — and the broader European sovereign-LLM movement has been operating without explicit answers to any of them.
The three questions form a structural feedback loop. Q3 (optimization target) determines Q2 (data volume needed) which conditions Q1 (openness sufficient for community contribution). The European sovereign-LLM movement collectively benefits from these questions becoming standard methodology disclosure, not exceptional critique.
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107 billion tokens. 5.8 billion clearly pt-PT.
The structurally tractable question with a structurally surprising answer. For a model whose entire stated purpose is European Portuguese prioritization, the native-language share of extended pre-training is 5.5%. The implications cascade into every other question.
The Olmo standard. AMÁLIA’s current state.
Allen Institute for AI’s Olmo project defines what “fully open” operationally requires. Olmo doesn’t lead frontier benchmarks. That’s not the point. The point is to be the structural reference for openness. AMÁLIA’s “fully open source” claim should track to the operational standard.
Four strategic positions. AMÁLIA between two and three.
Approximately €100M+ in publicly disclosed European sovereign-LLM funding across the major initiatives. The structural question every project faces: what is the actual competitive position you’re staking? Four options — none mutually exclusive — but each requiring different commitments.
Three standards. For AMÁLIA and the movement.
The structural critique generalizes beyond AMÁLIA. Italy, France, Germany, Switzerland, the OpenEuroLLM consortium, and every subsequent national project benefit from public discourse holding national LLM efforts to operational standards on openness, data accounting, and strategic positioning.
The European sovereign-AI agenda is a serious strategic project that deserves serious public discourse. O.Carmo’s analysis is what serious public discourse looks like. Appropriately diplomatic. Structurally rigorous. Willing to ask the hard questions in public when the public investment justifies it. More of this is needed — across every European sovereign-LLM project, not just AMÁLIA.
Implications for European AI Sovereignty
The questions surrounding AMÁLIA’s openness, native-language data, and strategic objectives reflect broader issues across Europe’s national AI initiatives. Addressing these questions is crucial for ensuring transparency, data sovereignty, and alignment with national priorities, especially as multiple countries develop their own models under similar structural constraints.
Unanswered, these questions could hinder Europe’s ability to develop truly autonomous, open, and strategically aligned language models, impacting its competitiveness and technological independence in the global AI landscape.
European Sovereign-Language Model Efforts and Challenges
European countries have launched several large language model initiatives, such as Italy’s Minerva, Germany’s Aleph Alpha, France’s Mistral, and others, often with public funding and strategic aims of reducing dependence on US and Chinese providers. These efforts face common structural questions about openness, data sufficiency, and goal-setting, which have yet to be fully addressed publicly.
Portugal’s AMÁLIA exemplifies this broader pattern: a significant investment resulting in a working model, but with unresolved questions about its openness, native data scope, and strategic purpose. These issues are central to the debate on Europe’s AI sovereignty and competitiveness.
“AMÁLIA is an impressive piece of work, but the hard questions about its openness and data remain unanswered.”
— Duarte O.Carmo
Unresolved Questions About AMÁLIA’s Openness and Goals
It is still unclear how open AMÁLIA will remain as development continues, and whether the model will meet European standards for transparency and data sovereignty. The strategic objectives behind its design and deployment are also not fully articulated, leaving critical questions open for future clarification.
Upcoming Milestones and Discourse on Structural Questions
The final version of AMÁLIA is scheduled for release in June 2026, which will likely provide more clarity on its capabilities and strategic positioning. Over the next 12 to 24 months, European researchers and policymakers are expected to scrutinize these structural questions further, potentially influencing future funding, regulation, and development strategies.
Key Questions
What are the main concerns about AMÁLIA’s openness?
Critics question whether the model will remain fully open to external scrutiny and whether its training data, especially native Portuguese data, is sufficient and transparent.
Why do the questions about native-language data matter?
Native-language data quality and quantity directly impact the model’s performance, fairness, and cultural relevance, making it a key strategic issue for European language models.
How does AMÁLIA compare to other European models?
While AMÁLIA outperforms previous open models on Portuguese benchmarks, it still trails some proprietary models like Qwen 3-8B on certain tasks, raising questions about its competitive positioning.
What are the broader implications for Europe’s AI strategy?
Addressing these structural questions is essential for Europe’s goal of developing autonomous, transparent, and strategically aligned AI systems, reducing dependence on non-European providers.
What will happen after the final version is released?
Expect increased scrutiny of the model’s openness, data use, and strategic goals, which could influence future policy, funding, and development directions across Europe.
Source: ThorstenMeyerAI.com
