In the previous section we hypothesized that deductive reasoning is emergent from the interaction between different inductive subsystems. Following this idea to its logical extreme suggests that if a model becomes large enough, then the line between deduction and induction becomes blurry. Large language models (LLMs) are a topical example. They are inductively trained on a large corpus of written knowledge (essentially nothing more than probabilistic next-token prediction with some expert task-alignment) but are clearly capable of deductive reasoning. A typical query of a LLM contains little to no information relevant to actually solving a problem (consider how many of the deductive properties in Figure are satisfied by a query like "why did the following python command result in a trace back? [code] "), nevertheless these models can respond by providing information external to the problem, answering when supplied with only partial information, and rejecting false premises.