C –> Java != Java –> LLM (www.observationalhazard.com)

Recent discussions in the AI community highlight the unique role of Large Language Models (LLMs) in software development, distinguishing their impact from previous programming language transitions. Unlike shifts from assembly to higher-level languages like C or Java, which transformed the intermediate product from source code to different paradigms and tools, LLMs still produce traditional code in languages such as Java, C, or Python. The source code remains the intermediate output, emphasizing that while LLMs enhance the development process—making tasks like refactoring and collaboration more efficient—they do not fundamentally change the underlying sources that developers work with. The significance of this distinction lies in the continued reliance on established programming languages and practices, meaning that while LLMs supercharge development workflows, they do not eliminate the need for human guidance or oversight. A noteworthy implication is the potential rise of dynamic interpreted languages in LLM-integrated systems, which may allow for real-time modifications to programs based on user prompts. This paradigm shift, where the traditional coding cycle evolves into a more fluid form of "vibe coding," could reshape software development, ultimately blending human creativity with the intelligent capabilities of LLMs.