Why their value for the IT market lies in reuse, open standards and avoiding vendor lock-in In the IT sector, we know well that a successful prototype is not necessarily a sustainable information system. It may work perfectly in a controlled environment, yet fail when it has to integrate with existing infrastructure, be maintained for … Read more
The future of AI is not something that will simply happen to us. It is the result of choices about who designs technology, who owns it, who controls it, and to whom they are accountable. One of the most dangerous ways we talk about Artificial Intelligence today is as if it were a natural phenomenon. … Read more
A language model built for a specific linguistic community AMALIA is an important example of what it means to build an open language model not as a generic commercial product, but as a public research infrastructure for a specific linguistic community. Its central goal is clear: European Portuguese should not be treated as a secondary … Read more
The trap of the US-China AI race The American debate on artificial intelligence is increasingly shaped by one simple but dangerous sentence: if we slow down, China will overtake us. This sentence has become a political shortcut. It allows major technology companies and their allies to describe regulation, safety testing and public accountability as threats … Read more
From models to industrial infrastructure The global race in artificial intelligence has moved beyond the competition for the best chatbot. The real battle is now over infrastructure: data centers, chips, power, memory, storage, networking, model-training pipelines, inference platforms and the cloud services that will deliver AI to billions of users. AI is no longer treated … Read more
The strategic shift The most important question in artificial intelligence is no longer whether a closed commercial API gives a slightly better answer on a benchmark. The real question is who controls the infrastructure, the data, the cost curve, the audit trail and the right to adapt the system. Open models, especially open-weight models, have … Read more
A breakthrough, not a final destination Large language models have already changed how people write, code, search, translate, summarize, teach and organize knowledge. Their success rests on a powerful empirical insight: when models, data and compute grow together, new capabilities appear. This is the core intuition behind the scaling hypothesis, and it explains much of … Read more
Most discussions about artificial intelligence begin with models. Which model is stronger, faster, cheaper or more capable? For public administrations and private enterprises, however, the decisive question is different: what data does the model reason over, who governs that data, how is it connected to real workflows, and how can every answer be traced back … Read more
Artificial intelligence is entering public administration, healthcare, education, local government and state security services. The central question is not whether public institutions will use AI. They already will. The real question is who will control the infrastructure, the data, the models, the logs and the rules of use. Public authorities can either build internal capacity … Read more
Europe does not suffer from a lack of open source. It suffers from a lack of large-scale adoption. Across the European Union, the policy direction is already clear. Interoperability, reuse and cross-border public service improvement are now central to Europe’s digital agenda. Open source has also been increasingly linked to reduced dependency, stronger digital autonomy … Read more
