Décider n'est pas générer : les modèles de décision
A decision model answers with a **typed value** (a category, a score, a choice from a closed list) plus a probability, whereas a generative model produces text that then has to be read or parsed. Much of a product's AI features write nothing: they classify a ticket, pick a path, score an answer, decide whether a human should step in. For those cases the distinction changes three things: 1. **Latency**: a decision fits in the time of a click, a generation makes people wait. 2. **Cost**: you pay for a short, bounded output, not a paragraph. 3. **Specification**: the feature is described by the list of its possible outputs, not by a prompt. An answer outside the list becomes impossible instead of unlikely. **The September 2026 signal.** TypeSafe announced Jev, its first public model, sold purely for deciding (70 to 500 ms end to end, typed output, calibrated probability), in waitlist early access. The numbers are the vendor's own, and Latent Space points out it does not replace an LLM: no free text, predefined outputs required. The category itself already exists under other names (classifiers, judges, routers), and that is what is worth keeping.