Coined in 1965, the "intelligence explosion" describes a runaway feedback loop. An AI capable of conducting AI research could use its intelligence to improve itself. This newly enhanced intelligence would make it even better at AI research, leading to exponential, uncontrollable growth in capability. This "fast takeoff" could leave humanity far behind in a very short period.

The most transformative aspect of AI may be its ability to automate its own research and development. This creates a recursive improvement cycle—an "intelligence explosion"—where progress accelerates exponentially, compressing decades of innovation into a much shorter period.

The first entity to achieve AGI could see it self-improve at an exponential rate, potentially achieving 20,000 years of progress overnight. This concept of "fast takeoff" makes any delay in the AI race, even for regulatory reasons, a potentially catastrophic strategic error.

The AI 2027 scenario hinges on AI systems becoming proficient enough at coding to accelerate AI research itself. This creates a powerful recursive self-improvement loop, leading to a rapid 'intelligence explosion' that progresses from full coding automation to broadly superhuman intelligence in approximately one year.

AI's ability to perform software engineering tasks that would take a human hours is doubling every 4-6 months. This rapid, exponential progress suggests a near-term future where AI can automate its own research and development. This self-improvement loop is the critical inflection point that could trigger a massive, unpredictable leap in AI capabilities.

While the long-term trend for AI capability shows a seven-month doubling time, data since 2024 suggests an acceleration to a four-month doubling time. This faster pace has been a much better predictor of recent model performance, indicating a potential shift to a super-exponential trajectory.

The key threat from AI isn't just its capability, but the unprecedented speed of its improvement. Unlike past technological shifts that unfolded over decades, AI agent autonomy on complex tasks has grown exponentially in just two years. This rapid acceleration is what financial systems and labor markets are not stress-tested for.

The AI 2027 model assumes progress is super-exponential, not just exponential. This means each successive doubling in an AI's capability (e.g., its time horizon for solving complex tasks) requires progressively less input. The curve steepens dramatically as AI approaches and surpasses human-level long-horizon planning.

The most significant AI feedback loop occurs when AI can perform its own research. This could expand the AI research workforce by 1,000x, dramatically accelerating progress and leading to more general-purpose AI far faster than linear trends suggest.