The thing I’ve noticed with LLM-assisted development is that the language ecosystem matters far more than people expect.
Once a codebase gets beyond a few thousand lines, models stop struggling with syntax and start struggling with entropy. If the ecosystem allows many ways to structure the same program, the model’s output begins to drift.
I’ve tried building non-trivial projects with LLM assistance in Java, JavaScript, Python, Rust, C++ and Go. The difference in how well the models hold the system in their "head" becomes obvious once you pass a few thousand lines of code.
Python and TypeScript in particular have a massive combinatorial space: frameworks, build systems, typing styles, dependency patterns, project layouts. Two teams solving the same problem can produce codebases that look completely different. That variability leaks into the training distribution and the model has to guess which universe it’s operating in.
Go sits at the opposite end of that spectrum.
There is essentially:
- one formatting style
- one standard build system
- one dependency mechanism
- one dominant project layout
- a very small set of concurrency primitives
- a language that has barely changed in a decade
That constraint turns out to be exactly what LLMs thrive on. The solution space is narrow, so the model converges instead of wandering.
In my experiments, once a project passes ~8k lines most languages start to show cracks: incorrect imports, wrong framework idioms, subtle API hallucinations. With Go the agents stay coherent much longer. Extremely fast build times and batteries-included tooling also tighten the feedback loop significantly.
Java often gets suggested as an alternative because it’s statically typed and mature. But in practice the ecosystem is a maze: Maven vs Gradle, Spring everything, annotation magic, layers of frameworks, multiple architectural styles. The language may be stable, but the surrounding universe isn’t.
I’m also a big Rust fan. If you genuinely need Rust’s capabilities it’s an excellent tool. But it’s slower to compile and significantly more complex for LLMs to work with. Beyond small codebases the difference becomes noticeable.
One thing that helps regardless of language is architecture. Strong modular boundaries, API-only feature access, and good code maps make a huge difference when working with LLMs.
Used carefully, these tools are a huge productivity multiplier. The interesting question now isn’t whether LLM coding works - it’s which languages, practices, and system designs allow it to work reliably at scale.