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.

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.

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.

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.

Unlike more abstract domains, AI research is particularly suited for automation by AIs. The tasks are verifiable, allow for iterative improvement, and can be broken down into containerized environments for reinforcement learning.

The next evolution for AI agents is recursive learning: programming them to run tasks on a schedule to update their own knowledge. For example, an agent could study the latest YouTube thumbnail trends daily to improve its own thumbnail generation skill.