I still like to understand the code, components, and structure if it’s something that will run in production

Two things to consider: - When reading generated code, which programming language would be the most readable to you? - Which programming language guides AI to write correct code using language features or guardrails?

There you will find your answer.

The article applies to a narrow case of a totally green field application that's going to be completely vibecoded. This is the only case where you reasonably can be indifferent to what the language is, and so you can abandon familiar Python and go with unfamiliar Rust. (If you _are_ familiar with Rust, the point of the article is moot.)

This "fair weather development" approach feels very risky if that application is going to be exposed to any serious usage. There WILL be a situation when things break and the AI will be powerless to fix it (quickly) without breaking something else in a vicious loop. There WILL be a situation where things work fine and tests pass with 3 concurrent users but grind to a complete halt with 1000 because there is something O(N^2) deep in the code. And you NEED a human to save your day (which requires also proper architecture for that to be possible in the first place). If you don't plan for this, and just hope for the best, then you are building nothing more than a toy. And if you plan for this, then it matters again what the language is, and whether your team is proficient in it.

Or maybe I too old fashioned or too behind the state of the AI art...

I haven't read the article (because I hate Medium) but I reckon the biggest reason why LLM-assisted projects use Python is because there is a metric buttload of python code on the web to be slurped up and used as training fodder.

Because AI creates unmaintainable messes in any language, and ergonomic ones help humans clean up.

And with AI writing code, why use libraries, which makes us more vulnerable to 0-day attacks?

Our simulation core components are pure Fortran, no libraries, all written by Claude/Cursor/Codex.

10-100x faster? Maybe for strictly IO bound applications - but if you are building a web app you won't see that performance as network latency dominates.

I also try to use Ruby because it's much more readable than anything else. And yes, still need to review and understand what code AI generated there

For the utilities I write it is faster to iterate without having to compile. When I get to the point where I'm done adding changing features, and performance is an annoyance I can always ask the AI to "rewrite this in Go". (I've never gotten to that point.)

    > it is faster to iterate without having to compile

You could answer this by asking yourself - Why not just let AI code in machine language?