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.

Unlike competitors with aggressive timelines for AI-driven research, Google's approach is practical. While Gemini helps improve itself, the immense cost and opportunity cost of large-scale training runs mean humans remain firmly in the driver's seat for critical decisions, making an autonomous "ML intern" unrealistic in the short term.

Beyond just coding, improving AI models requires subtle skills like designing effective reinforcement learning environments or managing human expert feedback. Newman questions how close we are to recursive self-improvement by asking if AIs can automate these tasks, which rely on nuanced "taste and judgment" rather than just raw computational ability.

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.