Wrangling with Explosive AI Growth – Combatting Catastrophism

Policymakers are accustomed to thinking in finite measurable terms like laws, budgets, and program implementation. Artificial intelligence, however, no longer advances in a straight line or within the familiar boundaries of public administration. Instead, it seems to accelerate along exponential curves, reshaping expectations faster than institutions can plausibly adapt. To many observers, it feels less like a set of discrete advances and more like an uncontrollable, or even frightening, open-ended growth event. First, I will draw historical comparisons to show how earlier “infinite” growth events, such as industrialization and the rise of the internet, led humanity to create new frameworks for understanding change. Then, I will use those historical parallels to inform how we should think about today’s AI growth event.

Past ‘infinities’ were stabilized by new conceptual frameworks around industrial production and digital communication. We should consider those frameworks in application to the exponential growth of knowledge itself that is being ushered in by Large Language Models (LLMs). The policy challenge ahead is not to restrain progress, but to encourage market mechanisms to allocate capital justly while establishing rules of the road that help policymakers work within this new terrain.

THE PAST

The Industrial Revolution represents the first example of what appeared to be an unrestrained economic growth event. Between 1750 and 1900, the world changed faster than anyone thought possible. The number of people on Earth tripled, the energy used jumped ten times, and factories doubled their output every couple of decades. For centuries, leaders and thinkers had conceptualized the economy as essentially cyclical.[1] Then the growth that came with fossil fuels and interchangeable parts forced us to rethink that conception.

Policymakers met this new scale of growth not by halting it but by inventing abstractions that made it manageable. Time standardization synchronized local hours into a global clock, the metric system replaced regional measures with a universal one, and national income accounting transformed countless transactions into aggregates like GDP, making industrial expansion both comprehensible and administrable. Yet even this apparent infinite growth mirage met its limits. Resource scarcity and diminishing returns imposed natural asymptotes, slowing exponential expansion into a gentler S-curve (see math appendix).

If the Industrial Revolution represented an infinity of matter and energy, the late twentieth century brought about an infinity of information. When computer scientists describe how efficiently an algorithm scales, they use “Big O notation” to characterize how runtime grows with inputs: for example, an O(n) process scales linearly, while O(2ⁿ) scales exponentially. The digital world, however, did not merely grow faster but grew in a way that resembled far steeper complexity classes. From 1980 to 2020, global digital storage capacity expanded by more than a billion-fold, and the number of connected devices followed something closer to a super-exponential curve, approaching O(n!) as each additional node multiplied the number of possible communication pathways. [2] This explosion of connectivity appeared boundless, an information singularity where the constraint shifted from physical scarcity to cognitive overload: how could finite human attention process effectively infinite data? Humanity did not try to halt this growth but shaped it through organizing frameworks. The TCP/IP protocol stack and HTTP standards organized how machines could talk to one another. PageRank[3] (the foundation for Google’s search algorithm) imposed order on chaos by mathematically assessing relevance, making a vast, unstructured internet navigable.

Over time, however, this informational infinity encountered a fundamental limit: human attention.[4] The number of connected devices and data streams continued to grow exponentially, but the hours available for humans to consume remained fixed. We are still amid this information growth event, but its trajectory, too, now bends toward an asymptote. Attention became the bottleneck that no increase in bandwidth or storage could overcome.

Shape of Successive Economic Growth Events