A complex system that works is invariably found to have evolved from a simple system that worked ~ John Gall
This essay continues the argument from Part I, Intelligence Too Cheap to Meter – Artificial Intelligence and the Rise of Intelligence as Infrastructure, which explored how abundant intelligence reshapes jobs, organizations, and institutions when intelligence itself begins to function as infrastructure.
More than a century ago, the sociologist Max Weber observed that modern society was undergoing a long and profound transformation in which tradition, intuition, and charismatic authority gradually yielded to systems organized around calculation, rules, procedures, and formal reasoning.
In earlier societies, much of life unfolded through inherited customs, religious frameworks, and the authority of individuals, whereas modern societies increasingly organize themselves through statistics, bureaucratic procedures, accounting systems, and forms of analysis designed to make the world measurable, predictable, and administratively legible.
This transformation, which Weber described as the disenchantment of the world, marked the gradual expansion of rational systems across science, industry, and public administration, as more and more domains of life came to be governed by processes of calculation, classification, and systematic reasoning.
Into this long historical arc enters a striking contemporary idea.
In a recent essay, Matt Webb described the possible trajectory of artificial intelligence using a phrase borrowed from an earlier technological dream: intelligence too cheap to meter.
During the early decades of the nuclear age, advocates predicted that electricity would become so abundant that measuring consumption would eventually become unnecessary, a vision captured in the phrase electricity too cheap to meter, and Webb’s adaptation of the expression invites us to imagine a similar transformation occurring in the domain of intelligence itself.
If the cost of intelligence continues to decline, intelligence ceases to function primarily as a scarce human resource and instead takes on the character of infrastructure, quietly embedded in systems that support economic activity, institutional coordination, and scientific discovery.
When intelligence becomes infrastructure, the most significant transformation lies not in the appearance of smarter tools but in the reorganization of institutions around abundant intelligence.
For most of modern history, the process of rationalization still depended upon human cognition, as bureaucracies required analysts to interpret data, corporations relied on managers to process information, and governments depended on experts to evaluate increasingly complex decisions.
Artificial intelligence alters that constraint.
As intelligence becomes widely available and computationally scalable, rationalization no longer depends primarily upon the limits of human attention and reasoning, because systems themselves can analyze, simulate, and optimize continuously across vast domains of activity.
Factories can operate through predictive coordination, supply chains can reorganize dynamically, energy grids can balance demand in real time, and scientific laboratories can explore thousands of hypotheses simultaneously.
Intelligence alone is not an advantage; intelligence connected to reality is.
Seen through Weber’s lens, artificial intelligence appears less as a sudden rupture and more as another stage in the long expansion of rational systems.
The scientific revolution rationalized the understanding of nature, the industrial revolution rationalized production, modern bureaucracies rationalized institutions, and the digital age rationalized the organization of information.
Artificial intelligence represents the next step in that trajectory: the rationalization of cognition itself, the moment when reasoning becomes scalable, and the automation of rationality begins to shape the structure of modern systems.
At moments like these, a broader economic pattern often becomes visible.
When a capability becomes extremely cheap, the bottleneck moves somewhere else.
Cheap electricity shifted the constraint to machinery and industrial design.
Cheap computing shifted the constraint to software and systems thinking.
Cheap information shifted the constraint to attention.
If intelligence follows the same path, the new constraints are likely to appear in coordination, legitimacy, trust, and judgment.
Artificial intelligence represents the next stage in the long process of rationalization that Weber described, extending systems of calculation and optimization from production and institutions into cognition itself.
Weber’s analysis, however, carried an important warning.
Rationalization generates efficiency and predictability while simultaneously constructing what Weber famously described as the iron cage, a social order increasingly structured by procedures, calculations, and impersonal systems that organize life according to administrative logic.
Artificial intelligence has the potential to intensify this dynamic as decision systems become more optimized, processes more automated, and institutions increasingly shaped by algorithmic coordination.
Hiring decisions, credit allocation, logistics networks, insurance pricing, urban planning, and policy design will rely on systems capable of continuous optimization, producing a world that is increasingly measurable, legible, and administratively structured.
Yet even in a civilization characterized by abundant intelligence, a fundamental boundary remains.
Machines can optimize systems, yet the determination of what those systems should optimize for remains a human question.
Values, goals, and legitimacy do not emerge from calculation alone.
Artificial intelligence extends the rationalized world that Weber described while simultaneously revealing the limits of rationalization itself, because a civilization equipped with vast computational intelligence continues to depend on the distinctly human capacity for judgment about what is worth pursuing.
In Part I, the focus was on how abundant intelligence reshapes work, organizations, and institutions.
In Part II, the transformation is placed within a longer historical arc: the rationalization of cognition itself.
Part III will explore the question that follows from this development: how societies decide what goals increasingly powerful systems should pursue, and how values, legitimacy, and governance shape the future of an age of artificial intelligence.