Sovereign AI-based Public Services are Viable and Affordable
Not on the map. Why?
Abstract (arXiv)
The rapid expansion of AI-based remote services has intensified debates about the long-term implications of growing structural concentration in infrastructure and expertise. As AI capabilities become increasingly intertwined with geopolitical interests, the availability and reliability of foundational AI services can no longer be taken for granted. This issue is particularly pressing for AI-enabled public services for citizens, as governments and public agencies are progressively adopting 24/7 AI-driven support systems typically operated through commercial offerings from a small oligopoly of global technology providers. This paper challenges the prevailing assumption that general-purpose architectures, offered by these providers, are the optimal choice for all application contexts. Through practical experimentation, we demonstrate that viable and cost-effective alternatives exist. Alternatives that align with principles of digital and cultural sovereignty. Our findings provide an empirical illustration that sovereign AI-based public services are both technically feasible and economically sustainable, capable of operating effectively on premises with modest computational and financial resources while maintaining cultural and digital autonomy. The technical insights and deployment lessons reported here are intended to inform the adoption of similar sovereign AI public services by national agencies and governments worldwide.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 20 references that are in the map. First posted on arXiv 2 Mar 2026.
Builds on
7 of 20 references.
References · 7
- 2025Small Language Models are the Future of Agentic AIcited by 254
- 2024Advancing Generative AI for Portuguese with Open Decoder Gerv'asio PT*cited by 19
- 2024The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder Early-stage Deliberations Around Public Sector AI Proposalscited by 15
- 2024Open Sentence Embeddings for Portuguese with the Serafim PT* encoders familycited by 4
- 2025SAIF: A Comprehensive Framework for Evaluating the Risks of Generative AI in the Public Sectorcited by 2
- 2025Towards Meaningful Transparency in Civic AI Systemscited by 1
- 2025Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challengescited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.