📊 Full opportunity report: AMÁLIA · The Three Hard Questions. on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
Portugal’s €5.5 million AMÁLIA language model is now operational, outperforming some benchmarks, but key structural questions remain unanswered. These issues have broader implications for European AI sovereignty efforts.
Portugal’s €5.5 million investment in the AMÁLIA language model has resulted in a functioning European Portuguese large language model, now available for academic use, but critical questions about its openness, native data, and strategic objectives remain unanswered.
AMÁLIA, developed by a consortium of approximately 60 researchers from Portugal’s top institutions, was officially completed in September 2025 and publicly announced in October. It is a continuation of the EuroLLM project, built on a multilingual foundation rather than trained from scratch. The model currently handles text in Portuguese only, with multimodal capabilities planned for future versions.
Technical evaluations show AMÁLIA outperforms previous open models on European Portuguese benchmarks and surpasses Qwen 3-8B on most Portuguese tasks, though it still trails on some specific benchmarks like ALBA. The model is used by 450,000 academic users across Portugal’s higher education system.
However, the analysis by Duarte O.Carmo and others raises three critical, structural questions about the model’s openness, native-language data sufficiency, and strategic goals, which are central to evaluating the broader European sovereign-LLM movement.
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.
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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.
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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.
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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 development and deployment of AMÁLIA highlight the strategic importance of national language models for Europe’s AI independence. While Portugal has made progress with a publicly funded, high-profile project, the unresolved questions about openness, data, and objectives could influence future policy and investment decisions across Europe. Addressing these issues is crucial for establishing a sustainable, competitive, and sovereign AI ecosystem within the continent.
European Sovereign-Language Model Landscape
Across Europe, countries like Italy, Germany, France, and Norway are developing their own language models, often with similar public funding and strategic aims. Many of these projects face comparable structural questions about how open their models truly are, how much native data is enough, and what the ultimate goals should be. The European sovereign-LLM movement is characterized by a pattern of public investments and ambitious goals, but lacks a unified framework for evaluating progress and addressing these core questions.
Portugal’s AMÁLIA serves as a case study within this broader context, illustrating both the potential and the challenges of building national language models that aim to balance openness, data sovereignty, and strategic relevance.
“AMÁLIA is an impressive piece of work, but it raises fundamental questions about openness, native data, and strategic goals that are yet to be answered.”
— Duarte O.Carmo
Unanswered Questions About AMÁLIA’s Structural Foundations
It remains unclear how open AMÁLIA truly is, given the partial disclosure of training data and model architecture. The sufficiency of native Portuguese data for long-term performance and adaptability is also debated, as the current dataset includes only about 5.8 billion tokens from Portuguese sources. Moreover, the strategic objectives—whether the model aims primarily for academic use, commercial deployment, or geopolitical influence—are still not explicitly defined or publicly communicated.
Further developments in transparency, data usage policies, and strategic planning are expected before the final release in June 2026, but details are still emerging.
Upcoming Milestones and Policy Implications
The final version of AMÁLIA is scheduled for release in June 2026, which will likely include more comprehensive evaluations and potentially greater transparency about data sources and model capabilities. Over the next 12-24 months, Portugal and other European nations will need to address the key questions of openness, native data sufficiency, and strategic goal-setting to ensure their models meet both technical and policy standards. Policy discussions around data sovereignty, open access, and AI governance are expected to intensify as these projects mature.
Key Questions
What makes AMÁLIA different from other European language models?
AMÁLIA is a publicly funded model built as a continuation of a multilingual foundation, focusing on European Portuguese, with a significant government investment and academic involvement. Its development approach contrasts with models trained from scratch, emphasizing strategic national language support.
Why are the three questions about openness, data, and goals important?
These questions determine whether the models are truly sovereign, how much native-language data is needed for effective performance, and what the models are ultimately designed to achieve—whether for academic, commercial, or geopolitical purposes. Addressing them is essential for transparent, sustainable AI development in Europe.
What are the risks of not answering these structural questions?
Without clarity, European models risk being limited in their openness, relevance, and strategic value, potentially undermining efforts for AI sovereignty and competitiveness. Lack of transparency could also hinder public trust and policy support.
Will the final version of AMÁLIA address these questions?
It is not yet clear whether the final release will resolve these issues, but ongoing evaluations and policy discussions are expected to push for greater transparency and strategic clarity before June 2026.
Source: ThorstenMeyerAI.com
