1. Go's spec and standard practices are more stable, in my experience. This means the training data is tighter and more likely to work.
2. Go's types give the llm more information on how to use something, versus the python model.
3. Python has been an entry-level accessible language for a long time. This means a lot of the code in the training set is by amateurs. Go, ime, is never someone's first language. So you effectively only get code from someone who has already has other programming experience.
4. Go doesn't do much 'weird' stuff. It's not hard to wrap your head around.
And then I find models try to write scripts/manual workflows for testing, but Go is REALLY good for doing what you might do in a bash script, and so you can steer the model to build its own feedback loop as a harness in go integration tests (we do a lot of this in github.com/humanlayer/humanlayer/tree/main/hld)
Also, strongly-typed languages tend to catch more issues through the language server which the agent can touch through LSP.