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
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, … 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
AI does not remove the need for understanding Artificial intelligence is already changing software development. Developers now use generative tools for autocompletion, refactoring, documentation, test generation, debugging and increasingly for agentic workflows where an AI system can inspect a repository, modify files and propose a pull request. This can be genuinely useful. It can reduce … Read more
From PHAROS to local models: a layered architecture for open AI The right strategy for artificial intelligence is not to choose one single technological solution. Not everything needs to run on a supercomputer, and it is equally unreasonable for every public body, university, school or business to depend permanently on commercial cloud APIs. The rational … Read more
The real issue is not intelligence, but authority AI agents are not just better chatbots. They are systems that can plan, call tools, write code, read documents, query databases, send messages, trigger workflows and sometimes act without direct human approval. That makes them valuable, but also institutionally dangerous. The key question for the public and … Read more
