Blame RMS for AI Coding (bit1993.bearblog.dev)

In a thought-provoking reflection, the relationship between Richard Stallman's advocacy for free and open-source software (FOSS) and the advances in AI coding has been highlighted. The argument posits that without the vast amounts of publicly available code from FOSS, large language models (LLMs) would lack the essential training data necessary for their coding capabilities. This irony emphasizes how foundational principles of FOSS have inadvertently fueled the development of AI technologies that now play a critical role in programming. The significance of this observation extends beyond mere acknowledgment; it raises important questions about the originality and creativity of LLM-generated code. Given that the code produced by LLMs draws heavily from existing FOSS, it prompts a reevaluation of how we perceive innovation in AI. Are LLMs merely synthesizing and recombining previously established knowledge rather than creating groundbreaking code? This perspective invites the AI/ML community to consider the implications of leveraging existing resources and challenges the notion of innovation in AI, ultimately reinforcing the influential role of free software in shaping both coding practices and AI advancements.