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.

Ajeya Cotra reports that leading developers like OpenAI, Anthropic, and DeepMind are converging on a strategy where each generation of AI is used to help align, control, and understand the subsequent, more powerful generation. This recursive approach is their primary plan for ensuring AI safety during rapid takeoff.

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.

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.

Unlike any prior tool, AI can be directly applied to improve its own creation. It designs more efficient computer chips, writes better training code, and automates research, creating a recursive self-improvement loop that rapidly outpaces human oversight and control.

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.

Companies like OpenAI and Anthropic are not just building better models; their strategic goal is an "automated AI researcher." The ability for an AI to accelerate its own development is viewed as the key to getting so far ahead that no competitor can catch up.

A 'skill' is a static AI command. A 'loop' elevates this by building a flywheel where the system learns from its own output and interactions, automatically feeding those learnings back to improve future performance.