A case for Go as the best language for AI agents

For the go vs rust points in the article:

- I agree that go's syntax and concepts are simpler (esp when you write libraries, some rust code can get gnarly and take a lot of brain cycles to parse everything)

- > idiomatic way of writing code and simpler to understand for humans - eh, to some extent. I personally hate go's boilerplate of "if err != nil" but that's mainly my problem.

- compiles faster, no question about it

- more go code out there allowing models to generate better code in Go than Rust - eh, here I somewhat disagree. The quality of the code matters as well. That's why a lot of early python code was so bad. There just is so much bad python out there. I would say that code quality and correctness matters as well, and I'd bet there's more "production ready" (heh) rust code out there than go code.

- (go) it is an opinionated language - so is rust, in a lot of ways. There are a lot of things that make writing really bad rust code pretty hard. And you get lots of protections for foot meets gun type of situations. AFAIK in go you can still write locking code using channels. I don't think you can do that in rust.

- something I didn't see mentioned is error messages. I think rust errors are some of the best in the industry, and they are sooo useful to LLMs (I've noticed this ever since coding with gpt4 era models!)

I guess we'll have to wait and see. There will be a lot of code written by agents going forward, we'll be spoiled for choice.

Strange article. Why is Go the best language for agents instead of, say, Python? Here are the points the author seems to make:

---

# Author likes go

Ok, cool story bro...

# Go is compiled

Nice, but Python also has syntax and type checking -- I don't typically have any more luck generating more strictly typed code with agents.

# Go is simple

Sure. Python for a long time had a reputation as "pseudocode that runs", so the arguments about go being easy to read might be bias on the part of the author (see point 1).

# Go is opinionated

Sure. Python also has standards for formatting code, running tests (https://docs.python.org/3/library/unittest.html), and has no need for building binaries.

# Building cross-platform Go binaries is trivial

Is that a big deal if you don't need to build binaries at all?

# Agents know Go

Agents seem to know python as well...

---

Author seems to fall short of supporting the claim that Go is better than any other language by any margin, mostly relying on the biases they have that Go is a superior language in general than, say, Python. There are arguments to be made about compiled versus interpreted, for example, but if you don't accept that Go is the best language of them all for every purpose, the argument falls flat.

> Haskell is great, for what it's worth, but as with any language you have to reign in the AI's use of excessive verbosity. It will stack abstractions to the moon even for simple projects, and haskell's strengths for humans in this regard are weaknesses for AI - different weaknesses than other languages, but still, TANSTAAFL

I haven't had this problem with Opus 4.5+ and Haskell. In fact, I get the opposite problem and often wish it was more capable of using abstractions.

Of my friend group the two people I think of as standout in terms of getting useful velocity out of AI workflows in non-trivial domains (as opposed to SaaS plumbing or framework slop) primarily use Haskell with massive contexts and tight integration with the dev env to ground the model.

Language models need redundancy (as informing structure). Not surprising, since they're trained on human language. It's hard to train a model on a language with a high entropy. I haven't tried it, but I think LLMs would perform quite badly on languages such as APL, where structure and meaning are closely intertwined.