📊 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 national LLM project, is operational and outperforms many models but faces three key questions about openness, native language data, and objectives. These issues have implications for Europe’s AI sovereignty.
Portugal’s €5.5 million government-funded project, AMÁLIA, an advanced European Portuguese language model, is now operational and publicly accessible, but key questions about its openness, data sources, and strategic goals remain unanswered, raising concerns about the broader European sovereign AI movement.
AMÁLIA was officially launched in October 2025, involving around 60 researchers from Portugal’s top research institutions, including NOVA and IST. The model is based on a continuation of the EuroLLM multilingual foundation, with the current version handling only text and knowledge up to the end of 2023. It outperforms previous open models on Portuguese benchmarks and beats Qwen 3-8B on most tests, though it still trails on some key measures.
Despite these technical achievements, questions persist about the model’s openness, the sufficiency of native-language data, and the primary goals guiding its development. These questions are not just technical but have policy implications, especially as Portugal and other European nations seek to establish AI sovereignty. The final version is scheduled for release in June 2026, but the current state of the project has already sparked debate among experts and policymakers.
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.
AI model training datasets Portuguese
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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.
openness in large language models
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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.
AI Engineering: Building Applications with Foundation Models
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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 and Policy
The development of AMÁLIA exemplifies the broader challenge facing European countries: balancing transparency, native-language data reliance, and strategic objectives in national AI projects. The unresolved questions highlight potential risks of overestimating openness and native data sufficiency, which could impact Europe’s ability to develop truly autonomous AI systems. The project’s progress and the answers it provides will influence future investments and policy decisions across Europe, shaping the continent’s role in global AI innovation.European Sovereign LLM Initiatives and Strategic Challenges
Across Europe, nations like Italy, Germany, France, and Norway are investing heavily in sovereign LLMs, often with similar structural questions about openness, native-language data, and purpose. Many of these projects, including Portugal’s AMÁLIA, are based on extending existing multilingual models rather than training from scratch, raising questions about the effectiveness of these strategies. The discourse has largely focused on individual model launches, overlooking the structural patterns and policy implications that these projects collectively reveal.
Public debates have centered on technical performance, but less attention has been paid to the underlying strategic choices, such as how open these models truly are and whether native-language data is sufficiently prioritized. Portugal’s investment and the public release of AMÁLIA have made these issues more visible, prompting calls for clearer standards and accountability in European AI development.
“AMÁLIA is an impressive piece of work, but it raises fundamental questions about openness and native data that need to be addressed publicly.”
— Duarte O.Carmo
Unanswered Questions About AMÁLIA’s Development and Goals
It remains unclear how open AMÁLIA truly is, especially regarding access to training data and source code, as well as the specific strategic goals guiding its development. The final version due in June 2026 may address some gaps, but current transparency levels are limited. Additionally, the sufficiency of native Portuguese data and how it impacts the model’s capabilities are still under debate, with some experts questioning whether the current approach adequately prioritizes native-language expertise.
Upcoming Milestones and Policy Debates
The next major milestone is the release of the final version of AMÁLIA in June 2026, which is expected to clarify some of the unresolved questions. Meanwhile, policymakers, researchers, and industry stakeholders will likely continue debating the model’s openness, native data reliance, and strategic purpose. These discussions will influence future funding, regulation, and development strategies across Europe, shaping the continent’s AI sovereignty trajectory.
Key Questions
What makes AMÁLIA different from other European LLMs?
AMÁLIA is a national project funded by Portugal’s government, based on extending an existing multilingual foundation, and is focused on European Portuguese. It is publicly accessible and outperforms many open models on Portuguese benchmarks, but questions about its openness and native data remain.
Why are the questions of openness and native data important?
They determine how transparent and autonomous the model truly is, impacting Europe’s strategic independence in AI. Openness affects reproducibility and trust, while native data reliance influences cultural and linguistic authenticity.
What are the risks of not answering these questions?
Without clear answers, European AI efforts could face challenges in establishing sovereignty, transparency, and competitive advantage, potentially leading to reliance on external models or incomplete strategic control.
When will we know more about AMÁLIA’s final capabilities?
The final version is scheduled for release in June 2026, which should provide more clarity on its openness, native data use, and strategic focus. Until then, assessments remain provisional.
Source: ThorstenMeyerAI.com
