Use boring languages with LLMs

A software consultant and researcher, Jacob of Sancho Studio, has emphasized that employing simple, consistent programming languages can significantly improve the reliability of large language models (LLMs) in coding and automation tasks. This insight underscores a shift toward favoring languages with fewer ecosystem variations to enhance AI performance.

Jacob notes that large language models tend to produce more predictable and accurate outputs when trained on corpora with low variability. Languages like Go, which maintain strict conventions and have a unified ecosystem, enable models to generate more consistent code snippets compared to more fragmented ecosystems such as JavaScript or Python. He highlights that the diversity in ecosystems introduces noise and unpredictability in inference, which can undermine the effectiveness of AI agents.

He points out that Go’s design—featuring a simple concurrency model with goroutines, a robust standard library, and enforced coding standards—makes it an ideal choice for AI training datasets. These factors lead to more reliable inference, as the model learns from a stable, predictable corpus. Conversely, languages with high fragmentation, like JavaScript or Python, present multiple competing patterns and package management systems, complicating the model’s ability to produce consistent results.

Why It Matters

This approach matters because it suggests a way to improve the performance and reliability of AI coding assistants and automation tools. By selecting languages with less ecosystem variability, developers can reduce the unpredictability of AI outputs, leading to more dependable software development processes and fewer debugging cycles. It also influences language design and ecosystem development, emphasizing the need for stability and convention to support AI integration.

Background

Prior to this insight, the focus in AI development centered on training larger models on broader datasets. However, recent observations from industry experts reveal that the quality and consistency of the training corpus—shaped by language choice—are crucial. Historically, fragmented ecosystems like JavaScript frameworks or Python package managers have posed challenges for AI inference. The 2024 State of JS survey highlighted the fragmentation within JavaScript, which complicates AI understanding and output reliability. This new perspective advocates for a shift toward languages like Go, which emphasize simplicity and standardization, to improve AI performance.

“Languages and ecosystems with low variance in their training corpus are represented better and executed more reliably by coding agents.”

— Jacob, Sancho Studio

“The model just is solving for which outcome is most likely, and consistent languages produce more predictable outcomes.”

— Jacob, Sancho Studio

What Remains Unclear

It remains unclear how much ecosystem fragmentation impacts AI inference in languages beyond those discussed, and whether future language design could mitigate these issues. The precise quantification of the benefits remains to be established through empirical studies.

What’s Next

Next steps include conducting systematic experiments comparing AI performance across different languages and ecosystems, and developing guidelines for language selection in AI training datasets. Industry adoption of more standardized languages like Go could increase, influencing language and ecosystem development.

Key Questions

Why does ecosystem fragmentation affect AI language models?

Fragmentation introduces variability in code patterns, package management, and ecosystem conventions, making it harder for models to learn consistent representations, which reduces inference reliability.

Are simpler languages always better for AI training?

Not necessarily; simplicity and consistency are key. Languages with strong conventions and minimal fragmentation tend to produce more reliable AI outputs, but the suitability depends on the specific application and ecosystem support.

Does this mean we should only use languages like Go for AI projects?

While Go shows advantages in consistency, the choice depends on project needs. The main takeaway is to prefer languages with stable, unified ecosystems for AI training and inference.

Will this approach influence future language design?

Yes, there is potential for language designers to prioritize stability and ecosystem coherence to better support AI integration and improve model performance.

Source: Hacker News