The question is not whether they “want” to deceive us When a large language model gives a false answer, it is not automatically lying. A hallucination is a generation error: the model produces an inaccurate statement because it predicts a plausible continuation without a reliable connection to reality. Deception is different. It is scientifically useful […]
From hallucinations to AI systems that can act The reliability debate around Large Language Models initially focused on hallucinations: plausible answers containing fabricated facts, sources or numbers. AI agents substantially change the nature of the problem. An agent does not merely generate text. It may search the web, call APIs, execute code, access files, query […]
Why the extraordinary expectations surrounding large language models have yet to show up in the economic data Large language models may be the fastest-diffusing digital technology in decades. Within only a few years, they have become everyday tools for hundreds of millions of people, entered enterprise software and triggered extraordinary investment in chips, data centers, […]
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 […]