AI Is Starting to Build Better AI – Recursive self-improvement is emerging (spectrum.ieee.org)

Recent advancements in artificial intelligence have reignited discussions about recursive self-improvement (RSI), a concept posited as early as 1966 by mathematician I. J. Good. RSI refers to AI systems' ability to not only enhance their outputs but also improve their own processes autonomously. While contemporary AI technologies—particularly large language models (LLMs) like GPT, Gemini, and Claude—can assist in developing better AI, they still depend heavily on human oversight for goal-setting and validation. The discourse around RSI highlights that while AI can incrementally refine itself through processes such as AutoML and machine-learning algorithms, the full closure of this self-improvement loop remains a work in progress. Recent examples, like OpenAI's GPT-5.3-Codex and Google DeepMind's AlphaEvolve, illustrate this evolution. These models can autonomously generate and debug code, playing significant roles in their development. However, they still require human input for decision-making on which problems to tackle and how to measure outcomes. The implications of these systems extend beyond mere coding efficiency; they represent a collaborative synergy between humans and machines, enhancing the potential for future AI breakthroughs. This development raises important questions about the trajectory of AI evolution and the balance between autonomy and human control in the design of intelligent systems.