The current way AI helps build better AI is more accurately described as an "autocatalytic effect"—using AI as a tool to accelerate development (e.g., creating GPU kernels). This is distinct from the sci-fi concept of "recursive self-improvement" (RSI), where a system holistically creates a better version of itself.

The AI development cycle of experimentation and bottleneck-solving is already a form of recursive self-improvement. Kyle Corbitt argues this loop is currently constrained by human intelligence. Once AIs become better at directing this process, progress will accelerate rapidly.

The concept that AIs can build better AIs, creating an accelerating feedback loop, is no longer theoretical. Leaders from Anthropic, OpenAI, and Google DeepMind have publicly confirmed they are actively using current AI models to develop the next generation, making RSI a practical engineering pursuit.

Once AIs reach human-level competence in AI research and development (R&D), a feedback loop could kick off where they rapidly improve themselves, compressing what would have taken 4-5 years of human-led progress into one.

Anthropic engineers now write eight times more code by instructing AI agents to do the work. This isn't just a productivity boost; it's a real-world example of recursive self-improvement, where the tools a company builds directly compound its own production capabilities, creating a feedback loop of acceleration.

Recursive aims to build superintelligence by creating an AI that can apply the scientific method to its own improvement. The goal is to automate the cycle of ideation, implementation, and validation of new AI research, enabling the system to recursively self-improve in an open-ended fashion.

The current approach to recursive self-improvement involves AIs gradually assisting human researchers, a stark contrast to Eliezer Yudkowsky's vision of a solo AI rapidly rewriting its own code. This modern, human-in-the-loop model is slower and offers more opportunities for safety checks and oversight.

Building an AI agent is the starting point, not the finish line. The real, ongoing work lies in optimizing its performance and training it on new information. This creates an essential new human-in-the-loop role focused on continuous improvement.

The viral claim of "recursive self-improvement" is overstated. However, AI is drastically changing the work of AI engineers, shifting their role from coding to supervising AI agents. This automation of engineering is a critical precursor to true self-improvement.