The concept of popularity among programming languages may become obsolete as artificial intelligence (AI) takes over coding tasks. This shift could make it harder to measure and rank languages based on their usage.
In the annual interactive rankings of top programming languages, Python has consistently topped the list, with JavaScript dropping from third place last year to sixth place this year. However, these changes may be due to AI’s impact on coding.
The rise of language-agnostic code generation using large language models (LLMs) means that programmers can focus less on the specifics of a language and more on the overall problem they’re trying to solve. This could lead to the demise of distinct programming languages as we know them.
As LLMs become increasingly capable, they’ll generate code in any language with minimal training data. This reduces the importance of choosing a specific language for a task, making traditional language preferences less relevant.
The impact on new language emergence is also significant. Currently, new languages rely on evangelizing their approach and gaining traction through samples, tutorials, and presentations. However, LLMs require vast amounts of data to improve, which can lead to poorer results when coding in lesser-used languages.
Researchers are working to develop more universal LLMs, but this doesn’t address the core issue of language popularity. Instead, it highlights that new languages need to address specific programming needs or provide a unique solution to an existing problem.
The future of programming languages will likely involve a shift towards abstracting away from language-specific details and focusing on high-level abstractions. This could lead to a more homogeneous programming landscape, where the role of the programmer is less focused on language choice and more centered around problem-solving and abstraction.
As we move forward, it’s essential to redefine what popularity means in this new era of AI-assisted coding. Metrics should prioritize problem-solving skills, high-level abstractions, and adaptability over traditional language-specific metrics.
Source: https://spectrum.ieee.org/top-programming-languages-2025