Why Go and other 'boring' languages produce better LLM-generated code

Jacob of Sancho Studio argues that LLM coding agents perform best on languages and ecosystems with low variance in their training corpus. Fragmented stacks like modern JavaScript and Python force models to gamble across dozens of equally weighted conventions — pip vs poetry vs uv, Django vs FastAPI, a dozen JS frameworks — and the resulting inference is unpredictable. Ecosystems with strong conventions, like Rails, produce more consistent agentic output not because they’re aesthetically superior but because cosine similarity in the training data is what drives next-token prediction.

Go emerges as the accidental winner of this moment. Its long-criticized resistance to language-level flexibility now pays off: goroutines sidestep the function-color problem entirely, the standard library covers most production needs without third-party churn, and gofmt/go vet/gopls enforce one canonical style that gives agents tight semantic feedback loops. Garbage collection avoids the memory-management fights that plague agent-written Rust, C, and C++, and Go’s bounded set of footguns keeps failure modes tractable.

The broader claim is that ‘convention over configuration’ was a productivity win for humans and is a far larger win for machines. As LLM-driven development scales, languages with consistent corpora and one-right-way tooling will increasingly outcompete more expressive but fragmented alternatives for non-visual backend work.