> A relative lack of training data might have a bigger effect though.

Nope. Not with Lisps. I've been using LLMs with Clojure/Clojurescript, Elisp and Fennel - for my personal stuff. And Python, Java, JS/TS, Go for work. LLMs are surprisingly good with Lisps, perhaps precisely because there's less fragmentation. There is so much variability with say Python for a given task, because the training set is enormous. But how many ways there exist to do the same thing in Clojure? Python/Java/TS's enormous training set is almost a liability for quality - the model has seen every beginner tutorial, every legacy pattern, every conflicting style guide. With Clojure it's more like the model learned from a curated corpus by default. Lisps have nearly zero syntactic noise - the AST is the source. This means an LLM doesn't need to learn parsing heuristics; structure is always explicit. That likely makes correct generation easier even with less data.