Unlike human-driven growth, which is limited by population and waking hours, AI agents can operate, replicate, and call each other endlessly. This creates a potentially infinite demand for compute infrastructure, far exceeding previous models and leading to massive, unpredictable strains on providers.

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."

Top researcher Andre Karpathy joined Anthropic not just as a star hire, but to lead a team using AI to accelerate AI research. This focus on "Recursive Self-Improvement" (RSI) suggests frontier labs believe they are close to a compounding loop where AIs design their successors, triggering an exponential acceleration in capability.

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 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.

Despite massive infrastructure investments, Greg Brockman believes demand for AI will consistently outstrip supply, leading to a long-term state of "compute scarcity." As AI tackles bigger problems like curing diseases, the appetite for computation will prove effectively infinite, making it a chronically scarce resource.

The current compute crunch isn't just a supply issue. It's because new AI models are so much more capable that they unlock a total addressable market (TAM) of valuable tasks that grows exponentially, far outpacing the linear or geometric growth of compute supply.

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.

The shift from simple query-based AI to agentic AI, where AI calls itself recursively to solve complex tasks, increases compute demand by orders of magnitude. Most people, especially non-coders, fail to grasp this exponential shift, leading them to consistently underestimate the scale and duration of the AI infrastructure build-out.