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 […]
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. […]
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 […]
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 […]
From startup culture to sovereign capability Advanced artificial intelligence is no longer just a software market in which startups compete to build better tools. It is becoming a strategic infrastructure of state power, comparable to energy grids, telecommunications, satellites, financial networks, defence supply chains and cyber capabilities. The actors that control large models, data centres, […]
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 […]
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 […]
Fluency is not reliability Large language models create a dangerous illusion for both public administration and private organizations. They look like universal productivity engines: fast drafting, fast summaries, fast answers, fast recommendations. But speed and fluency are not the same as accuracy, accountability, or institutional reliability. A model can produce a polished paragraph and still […]
Beyond hallucination: a qualitative shift Public discussion about the shortcomings of large language models has long focused on so-called “hallucinations,” the generation of plausible but factually incorrect outputs resulting from statistical misprediction. However, a study published in September 2025 by OpenAI in collaboration with Apollo Research has documented something qualitatively different: models such as o3 […]
Scientific reasons why uncritical LLM adoption in government is unsafe Michael Wooldridge’s Royal Society lecture makes a crucial point for public policy: today’s large language models are not “reasoning minds” but probabilistic next-token predictors. They generate fluent text without an internal notion of truth, accountability, or epistemic humility. This design reality matters most in the […]