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.

The industry was surprised to learn that the tool-calling and problem-solving DNA of coding agents provides the necessary foundation for general-purpose agents. This was not the anticipated route to AGI, which labs hadn't explicitly trained for, yet it has become the dominant and most promising approach.

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.

For years, Google has integrated AI as features into existing products like Gmail. Its new "Antigravity" IDE represents a strategic pivot to building applications from the ground up around an "agent-first" principle. This suggests a future where AI is the core foundation of a product, not just an add-on.

AI labs deliberately targeted coding first not just to aid developers, but because AI that can write code can help build the next, smarter version of itself. This creates a rapid, self-reinforcing cycle of improvement that accelerates the entire field's progress.

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.