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.

Silicon Valley insiders, including former Google CEO Eric Schmidt, believe AI capable of improving itself without human instruction is just 2-4 years away. This shift in focus from the abstract concept of superintelligence to a specific research goal signals an imminent acceleration in AI capabilities and associated risks.

Jack Clark of Anthropic estimates a 60% probability of achieving end-to-end automated AI R&D by 2028. This "recursive self-improvement," where AI designs better AI, would mark a critical threshold, leading to an intelligence explosion and a future that is nearly impossible to forecast.

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 key strategy for labs like Anthropic is automating AI research itself. By building models that can perform the tasks of AI researchers, they aim to create a feedback loop that dramatically accelerates the pace of innovation.

The ultimate goal for leading labs isn't just creating AGI, but automating the process of AI research itself. By replacing human researchers with millions of "AI researchers," they aim to trigger a "fast takeoff" or recursive self-improvement. This makes automating high-level programming a key strategic milestone.