For decades, software has been a static artifact — written by humans, tested by humans, debugged by humans, and eventually retired when the cost of maintaining it exceeds the cost of replacing it. But a growing body of thought, now gaining traction among AI researchers and software architects alike, suggests that the next generation of programs won’t just run — they’ll learn, adapt, and improve themselves without waiting for a developer to push an update. The concept of self-improving software isn’t new in academic circles, but it has remained largely theoretical until recent advances in machine learning, large language models, and autonomous agent frameworks brought it closer to practical reality. A detailed exploration published by Contextual Alignment , a technical publication focused on software architecture and AI alignment, lays out a framework for understanding what self-improving software actually means, how it might work, and what risks it carries.…