The vague concept of AGI is being replaced by Recursive Self-Improvement (RSI)—AI models creating their own successors. This is seen as a more specific and potentially nearer-term threshold that could trigger an uncontrolled explosion in AI progress, moving humans "out of the loop entirely."

The fear of runaway Recursive Self-Improvement (RSI) is tempered by economic reality. Training frontier models is becoming more, not less, expensive and complex. Each new model requires more GPUs, time, and brittle engineering, creating a logistical barrier that naturally slows progress.

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.

Recursive self-improvement is dangerous in four key ways: 1) AI capabilities outpace safety research, 2) a misaligned AI will build misaligned successors, 3) society skips learning from less-powerful intermediate AIs, and 4) it creates winner-take-all dynamics that encourage reckless racing between labs.

The recent calls to "pace the frontier" by leaders from Anthropic and OpenAI are directly linked to models beginning to exhibit recursive self-improvement—the ability to design their own, more powerful successors. This capability accelerates progress beyond predictable scaling laws, creating uncontrollable risks.

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.

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 concept of Recursive Self-Improvement (RSI), where AI models help train the next generation, has created significant anxiety among AI researchers themselves. The conversation has evolved from AI automating software engineers to researchers questioning if their own roles will soon be obsolete.