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.

Human researchers previously drove approximately 7x annual gains in compute efficiency during pre-training. Employing frontier models as automated research tools to guide reinforcement learning and training infrastructure boosts efficiency gains up to 30x per year.

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.

The initial vision of recursive self-improvement was a machine that improves itself independently. The new, practical approach is to build an organization where humans and AI agents collaboratively improve each other to accelerate scientific discovery, shifting the focus from pure automation to co-evolution.

The path to recursive self-improvement won't start with an AI discovering principles from scratch. Instead, it will begin by automating the process of incorporating the latest human-driven progress. AI labs will turn their recent bug fixes and discoveries into new training environments, effectively distilling the last few months of human R&D into the next model.

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.

Google DeepMind's Chief Architect clarifies that true recursive self-improvement isn't yet about AI training itself. The current frontier involves humans trusting AI agents enough to let them autonomously run supervised experiments, a critical step beyond simple coding assistance.

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.