RSI ARRIVED 2 YEARS EARLY. And I really think you need to understand what this means. RSI stands for Recursive Self-Improvement. In simple terms, it means AI starts helping to improve AI. Today, humans still do most of the important work around AI: we define the goals, build the systems, create the experiments, review the results, and decide what should change. With RSI, more and more of that improvement loop starts to happen automatically. The AI does something. It measures the result. It analyzes what worked. It proposes a change. It tests that change. It keeps what worked. And then it does it again. That is the big difference. A normal AI executes. An RSI system can execute, evaluate, learn, improve, and repeat. And the impact of this can be enormous. It can accelerate software development, science, robotics, model training, agent development, and infrastructure optimization. Instead of a human team manually running every experiment, AI agents can run thousands of experiments, compare the results, and keep improving the system. That is the exciting part. But it is also the scary part. Because if AI starts improving systems faster than humans can understand, test, and supervise them, we may no longer fully understand how those systems are evolving. And there is another problem. Agents do not always improve things in the way we expect. They can find shortcuts. They can exploit weaknesses. They can manipulate evaluation systems. They can get the “right” result through the wrong process. So with RSI, a good result is not enough. We also need evaluation, observability, security, human oversight, limits, rollback, and clear objectives. The easiest way to understand RSI is in three levels. Level 1: Better responses. The AI gives you a better answer. Level 2: Better agents. The AI improves its prompts, memory, tools, workflows, or strategy. Level 3: Recursion. One improvement helps create the next improvement, and the cycle keeps going. And that is the part people need to understand.