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
Why AI Still Fails at Common Sense Fluent language is not understanding Modern large language models can draft reports, summarise legal documents, write code, translate texts, answer questions and coordinate multi-step tasks with impressive speed. This fluency creates a dangerous illusion: because the answer sounds human, users often assume that the system understands like a … Read more
The crisis is deeper than cheating Generative artificial intelligence has not invented the crisis of university assessment. It has exposed it. For decades, higher education has relied heavily on essays, take-home assignments, reports and exams designed around one basic assumption: the text submitted by a student is a reliable proxy for that student’s understanding. That … Read more
From better retrieval to answer inspection before delivery Retrieval-Augmented Generation was introduced as a practical answer to one of the central weaknesses of large language models: their ability to produce fluent, confident text even when they do not know the answer. The core idea is straightforward. Before the model answers, the system retrieves relevant documents. … Read more
AI changes the economics of attack Cybersecurity has entered a new phase. The main shift is not that computers suddenly became vulnerable. Software has always contained bugs, and some of those bugs have always been exploitable. What has changed is the economics of attack. Artificial intelligence lowers the cost of finding weaknesses, understanding unfamiliar code, … Read more
The race is not only about technology The current artificial intelligence boom is often described as a technological race. That description is incomplete. It is also a race for infrastructure, market power and control over the next layer of the digital economy. The largest technology companies are investing hundreds of billions of dollars every year … Read more
The shift from assistant to operator For a long time, public discussion treated artificial intelligence as an assistant. You ask, it answers. You upload a document, it summarizes it. You request a draft, it proposes one. That framing is already outdated. The real security issue begins when AI stops waiting for instructions and starts operating … Read more
Convenience is not the same as learning Large language models are now part of everyday life in education, work, and public communication. They draft text, summarize documents, suggest ideas, organize arguments, and respond instantly to complex questions. Their usefulness is obvious. But that usefulness becomes a problem when speed replaces effort, and assistance turns into … Read more
