LLMs Exhibit Strong Python Bias, Favoring Established Libraries Over Newer Alternatives

San Francisco, CA – Coding agents, powered by large language models (LLMs), appear to perform significantly better when working with Golang compared to Python or TypeScript, according to an anecdotal observation shared by Kyle Wild on social media. Wild attributes this disparity to Golang's explicit nature, which allows for "less magic," and the rapid "library/coolness churn" prevalent in the Python and TypeScript ecosystems, which can render LLM training data quickly outdated. Research supports the notion that LLMs often exhibit a strong bias towards Python, frequently defaulting to it even when other languages might be more suitable for a given task. A recent study, "LLMs Love Python," found that models heavily favor Python for code generation, using it in 90-97% of cases for benchmark tasks and remaining the most-used language in 58% of instances for project initialization, even when Python was not the optimal choice. This bias is suspected to stem from the prevalence of Python in LLM training data. The issue of "library churn" in Python and TypeScript, as highlighted by Wild, is a recognized challenge. LLMs have been observed to suggest deprecated API calls due to their training data not keeping pace with rapid library evolution. This can lead to the generation of outdated or inefficient code, increasing integration costs and maintenance issues for developers. Conversely, the explicitness of languages like Golang and the type safety offered by TypeScript provide distinct advantages for building robust, production-grade AI systems. While Python excels in rapid prototyping and boasts a vast ecosystem for data science and machine learning, its dynamic typing and Global Interpreter Lock (GIL) can introduce runtime bugs and complicate concurrency management in large-scale applications. TypeScript, with its compile-time checks, can prevent a class of bugs before they reach production, enhancing reliability and developer productivity. The tendency of LLMs to favor established, sometimes older, libraries over newer, high-quality alternatives also raises concerns about code homogeneity and stifling innovation within the open-source community. This bias, driven by training data, can lead to less diverse and potentially suboptimal solutions for developers relying on LLM-generated code.