Latest Articles
AI Catch-22
Six months ago in AI looks like a medieval era. Three researchers from METR spent six days analysing the July agent incident at OpenAI and Hugging Face. The instrument they used was another frontier AI. That is the paradox. But then a harder question follows. What if the analyst AI has itself been poisoned? Three parts: the METR incident and the poisoning question; how to think about Zero Trust and MDR, with specific things to demand from your vendors in writing; and how to filter the wider AI reading list, from mathematics to cheating to rebuilding malware to the warning that this ends in people being killed.
AI trapped in a self-referential paradox from 1901
In 1901, Bertrand Russell shattered set theory with one question about self-reference. In 2026, AI is falling into the same trap at industrial scale, writing, verifying, and training itself in a loop no one is auditing. Watermarking is a first step, not a fix. By 2030, a child asking “is this true?” may get a fluent, confident answer with no human at its root. Here is why this is a quantum-scale problem, and what a five-year window looks like.
The Great Illusion of AI Provenance
Every major AI company points to provenance as a saving grace. We track our data. We log our training. We can show you the chain of custody. It sounds reassuring. It sounds like science. In its current form, it is an illusion. The technical layer is broken. The business layer is opaque by design. The jurisdictional layer has no single answer. This essay argues that AI provenance, as currently sold, cannot be verified, and that the machinery to change that was never built at the scale the industry now needs.