Contrary to 'fast takeoff' theories of a sudden AI explosion, the transition to superintelligence will likely be a slower, decade-plus process. While models will improve rapidly, the messiness of the real world—ambiguous tasks, distributed context, and institutional inertia—will slow the diffusion and impact of the technology.

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.

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.

While AI progress is marketed in revolutionary "step-changes" (e.g., GPT-3 to GPT-4), the underlying reality is more like compounding interest. A continuous stream of small, incremental improvements are accumulating, and their combined effect is what creates the feeling of an exponential leap in capability over time.

A significant belief shift has occurred among top AI researchers in the last 12 months. Many now feel that recursive self-improvement in models means exponential intelligence is just a couple of years away, a notable acceleration from previous, longer timelines.

The expert inside view is that RSI will likely manifest as another significant acceleration in AI capabilities—a "kink" in the progress curve—rather than a sudden, discontinuous singularity. This informs a more measured, though still urgent, approach to planning.

Once AI systems become proficient at AI R&D, they can trigger a recursive self-improvement loop. This process could radically accelerate progress, potentially achieving an amount of algorithmic advancement in a single year that previously took over a decade. This "intelligence explosion" could rapidly create wildly superhuman systems from a starting point of mere human-level competence.

Artificial General Intelligence—AI surpassing humans in most tasks—will be a gradual process, not a sudden, announced moment. It will "sneak in on us" as capabilities incrementally improve, without a clear before-and-after societal shift.

Unlike pure mathematics which is limited only by thought, recursive self-improvement in AI (RSI) is bottlenecked by the time and resources required to run physical experiments like training new models. This physical constraint means progress is a series of serial steps, not an instantaneous intelligence explosion.