If AI writes your code, why use Python?

niek_pas

Bit off topic but why in the world are people still posting on medium? The reading experience is abhorrent; I couldn’t even finish reading this article before a full screen popup literally blocked the sentence I was reading. Is there some incentive I’m not seeing?

schmookeeg

I assume this is why things like PyO3 are popping up? If so, sort of a fascinating way to compartmentalize new rust code into legacy .py code in lieu of a refactor, or at least, a way to do a staggered refactor and eat the elephant in bites :)

tontinton

Also easier to ship a binary like a cli

lenerdenator

1) I still have to comprehend it. 2) The corpus for the sort of applications I build is likely larger for Python than it is for C++ and Rust. Bigger corpus == more training data == better generated code. 3) The bottleneck in the applications I run aren't in the execution of the code; they're in the database/network latency. 4) I don't get anything extra for pushing Rust or C++ over Python.

kylec

This post resonates. I recently built a little web service to scratch an itch I've been having and after discussing the options with Claude we settled on Go, and honestly it's been fantastic. Highly performant, native threading, dead simple to deploy with containers. And I don't even know how to read or write Go.

infinite_spin

For me, whether it's AI or my own handcrafted artisanal code, the choice of language comes down to what has the least friction. This means I turn to vite/react for a lot of frontend requirements, and that the backend will be in nodejs or python, because those are easier for me to debug than writing an equivalent application in C++ or Rust.

xnx

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.)

rchowe

Python has a much more mature ecosystem than Rust, especially for AI/ML stuff. I ran into a rust crate that purported to do a certain ML algorithm but did not do it correctly. I managed to write a replacement with Claude though. I do think enforcing correctness at the type system level is a good idea for AI, which is why I often choose languages like C# and Rust over Python. However, for some things Python is definitely the correct tool for the job.

avereveard

https://arxiv.org/pdf/2508.09101 tldr 2% average point lost on Rust compared to python, gap vary by model, go has a better upper bound but opus had it 3% below python. benchmark is a bit old but research on why is there, article is just vibes

GardenLetter27

The LLMs just churns out non-idiomatic slop in any language. It doesn't matter if the 800-line if statement is able to use pattern matching. There's been a lot of progress on making coding agents able to solve problems when they can easily evaluate in a closed loop, we desperately need something similar for controlling complexity and using relevant abstractions.

an0malous

The ideal language for AI coding: 1. Type safety as basic guard rails that LLM output is syntactically and schematically correct 2. Concise since you have to review a lot more code 3. Easy to debug / good observability since you can't rely on your understanding of the code. Something functional where you can observe the state at any moment would be ideal. 4. A very large set of public code examples across various domains so there's enough training data for the LLM to be proficient in that language 5. A large open source ecosystem of libraries to write less code and avoid the tendency for generated code to bloat It's basically all the same things you look for in general. I think TypeScript scores high here but I'm curious if anyone knows of a language that fits these criteria better.

__mharrison__

AI's are really good with Python. Quick turnaround. Easy to read. Tons of training data/examples. Many of the same reasons we wrote Python before. Another benefit to using Python, is if you subscribe to writing/vibing a throwaway version first, a Python version is 100x better than a spec. (Disclaimer: I teach Python and AI for a living and am doing a tutorial at pycon this week, Beyond vibe coding. Am also using other languages as there are times when Python isn't appropriate)

fxj

You can of course use any language but here is my advice: you should use the language that you know best to make your life as uncomplicated as possible when you want to understand what the LLM was creating. Remember, you are the judge whether the code is OK and if you use assembler you might get really performant code, but can you trust it? Of course it might be a good incentive to learn rust or go. Or challenge yourself to learn something really cool like LISP, COBOL, FORTRAN, APL or J. (just kidding...) just my 2 ct...

CivBase

This point only makes sense if you ship AI code without reviewing it. And if you're shipping AI code without reviewing it, you're going to run into much bigger problems than Python performance limitations.

0xbadcafebee

I know a couple languages fairly well: C, Perl, Python, Bash. I never formally learned Go, but as a test of AI coding, I started some vibe coded projects in Go. It worked very well: the code is minimal, there's few dependencies, and it compiles down to a static app. But most importantly, I can actually read the Go code and understand basically what it's doing. I can also use LLMs to critique the code if I'm uncertain. The big benefit of Go is the simpler language and "batteries included" standard library. This leads to fewer dependencies and less lines of code, which improves overall AI output. In theory, AI should be able to write better code faster in Go than in another language like Rust. Python does have a much larger ecosystem of course, so with Go you have to develop from scratch what already exists in Python. But for smaller projects, you can also have an AI write a clean-room implementation in Go of some project in Python. So you aren't necessarily locked into one ecosystem anymore. And in my experience, you don't even need to know the language. I have a co-worker who's basically not a programmer, but got multiple implementations of applications working sooner than our dev teams doing it by hand. You should be a coder so you can architect and orchestrate the coding, but 'language' isn't a barrier anymore.

serf

1) python is one of the foremost trained upon languages 2) it's practically verbose, not technically 3) it resembles pseudocode 4) batteries included shortcuts a lot of work all of these reasons are a boon for LLM work.

bad_username

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...

munro

Lately I just have Claude build most things in Rust, it's really amazing. I tried Go, but I found it wasn't as good--Rust really does to me feel like Python. That said, it still struggles with the same class of errors of building complex systems. I've tried using TLA+, Alloy, and other things but haven't found the trick yet. The best I've found is reimplementing all external systems in memory and e2e testing everything extensively, without reimplementing the tests become unusably slow, and Claude can rewrite huge surface areas with ease--it's somewhere between mocking and literally just reimplementing the external systems.

skybrian

This seems sort of like asking whether a chatbot should answer you in English or Japanese. Obviously, it should use whichever language you understand. If you understand Python best, why not write code in Python? But on the other hand, maybe you could learn some other programming language, particularly with AI help. If that's what you wanted to do anyway, it seems like a good time to learn.

ActorNightly

a) Python (and Node) comprise the largest training set for all the models, so you are likely to get way better accuracy, especially with local models b) Python code is easier to introspect, and set up test harnesses around. And also extend in agentic frameworks c) LLMs are really good at translation. I can give it python code and it can translate it into C.