--- title: "The Programming Languages AI Still Gets Surprisingly Wrong" url: https://daily.dev/posts/the-programming-languages-ai-still-gets-surprisingly-wrong-lbxoqyzy8 source_url: https://code.likeagirl.io/the-programming-languages-ai-still-gets-surprisingly-wrong-fbad00b269f6 type: article source: "Code Like A Girl" published: 2026-08-25T02:11:40.272Z updated: 2026-08-25T02:12:07.853Z tags: ["python", "rust"] reading_time: 14 upvotes: 1 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # The Programming Languages AI Still Gets Surprisingly Wrong **[Code Like A Girl](https://daily.dev/sources/colkgirl)** · 14 min read · 1 upvotes · 0 comments ## Summary AI coding assistants generate syntactically plausible code across nearly every language, but fluency varies sharply by how heavily represented a language is in training data and how strict its compiler and memory model are. Python benefits from massive representation and forgiving syntax, but studies like EffiBench-X show it also yields more efficient code than Java, C++, or Go, while DeepSeek-R1 posted similar correctness on C++ and Python but notably better efficiency on Python. C and C++ allow memory bugs that compile cleanly and surface only under specific conditions. Rust's strict borrow checker exposes mistakes immediately, but a HackerNoon benchmark found the Python-Rust gap wasn't statistically significant, with Elixir showing the real gap instead; RustEvo² found a knowledge-cutoff effect where models scored 56.1% on pre-cutoff APIs versus 32.5% on newer ones. SWE-bench Pro shows frontier models scoring near 23% versus 70-80% on SWE-bench Verified, highlighting how benchmark design affects perceived capability. The piece recommends specifying language versions, stating constraints upfront, and treating models as collaborators to push back on rather than oracles. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why does Rust seem harder for AI coding assistants than Python even though benchmarks show similar accuracy? The performance gap between Python and Rust isn't statistically significant according to a HackerNoon comparative analysis of 100 recent problems from October 2025 to February 2026, chosen to avoid memorized problems. The much larger, clearly significant gap in that study appeared with Elixir instead. Rust's strict borrow checker just makes failures more visible immediately rather than the underlying error rate being dramatically higher. _daily.dev surfaces language-specific AI coding research for developers comparing tool reliability across ecosystems._ ### How much does an AI model's knowledge cutoff affect its accuracy with newer Rust APIs? Models averaged 56.1% success on Rust standard library and crate APIs available before their training cutoff, compared with only 32.5% on APIs introduced afterward, according to the RustEvo² benchmark evaluating 588 API changes. Giving the model retrieval access to current documentation improved performance on those newer APIs by an average of 13.5%. _Track how fast-moving API changes affect AI-assisted coding reliability with daily.dev._ ### Why does AI-generated Python code run more efficiently than AI-generated C++ code even with similar correctness rates? DeepSeek-R1 had nearly identical Pass@1 correctness on C++ and Python, 75.12% versus 74.64%, but its execution-time efficiency was noticeably better on Python at 67.30% of human reference performance versus 60.89% on C++. The EffiBench-X benchmark found models consistently produce more efficient code in Python, Ruby, and JavaScript than in Java, C++, and Go, likely reflecting training data representation rather than raw capability. _daily.dev helps developers weigh AI code efficiency tradeoffs before picking a language for a task._ ## Similar posts on daily.dev - [AI and machine learning outside of Python](https://daily.dev/posts/ai-and-machine-learning-outside-of-python-jykbfnjcf) · InfoWorld · 0 upvotes · 0 comments --- Tags: [#python](https://daily.dev/tags/python), [#rust](https://daily.dev/tags/rust) [View this post on daily.dev](https://daily.dev/posts/the-programming-languages-ai-still-gets-surprisingly-wrong-lbxoqyzy8)