Most laws aimed at the most powerful AI models decide who is covered by counting training compute. The unit is FLOP, the total number of floating-point operations used to train a model. This page compares the thresholds now written into law, what each one triggers, and when the obligations start. Last updated: 15 September 2026. The short version The EU sets its line at 10^25 FLOP. California, New York, Illinois and South Korea all use 10^26, which is ten times higher.

AI sovereignty is the capacity of a state to develop, deploy and govern artificial intelligence systems without depending on infrastructure, models or rules controlled by another state. The term entered mainstream policy vocabulary around 2023 and has since been applied to national compute programmes, domestic model development, data residency law and regulatory regimes. It is used loosely, and that looseness matters: the phrase bundles together at least four distinct claims with radically different price tags and success rates.

I like open-weight models. I think Jensen Huang’s argument — that open weights let more eyes find more flaws faster — is broadly correct as a security philosophy, and I’ve made versions of that argument in print before this week. I still believe it. I just don’t think this week is the evidence for it that the industry’s letter wants it to be. Meta, Microsoft, OpenAI and others signed a letter defending open-weight models against “premature restrictions,” timed to Huang’s first-ever post on X making the eyes-on-the-code case.

A working reference for the vocabulary of modern AI — organized by conceptual layer, not alphabetically. Each definition is written for practitioners and informed generalists: precise enough to cite, plain enough to share. Foundation Layer Large Language Model (LLM) A neural network trained on vast quantities of text to predict and generate language. LLMs learn statistical patterns across billions of documents, enabling them to answer questions, write code, summarize text, and engage in dialogue.