To understand the trajectory of artificial intelligence, we must begin with its governing physics. Every technological revolution has a medium in which it travels most efficiently. Steam traveled through pressure vessels. Electricity traveled through conductive metals. Software traveled through silicon. AI travels through something even more elemental: the binary substrate of 1 and 0.
This is the gravity of the digital world.
AI excels at anything that can be fully translated into symbolic representation—text, code, structured data, probabilistic inference. These domains share a common trait: they are abstractions. They exist as information, not matter. Once a process becomes informational, it becomes compressible. Once compressible, it becomes optimizable. Once optimizable, it becomes automatable.
This is why AI’s earliest and most dramatic disruptions appear in software engineering, legal research, financial analysis, customer support, and content production. These industries are fundamentally symbolic. Their raw material is not steel, but syntax. Their supply chains run through servers, not ships. Their outputs can be copied infinitely at near-zero marginal cost.
AI does not disrupt these industries arbitrarily. It disrupts them because they exist within its native medium.
But this observation immediately raises a second, more important question: Where does AI lose its leverage? Where does the gravity weaken?
The answer lies in the boundary between bits and atoms.
While AI can reason about the physical world, it cannot instantiate it. It can design a bridge, but it cannot pour concrete. It can simulate a tire compound, but it cannot vulcanize rubber. It can optimize a power grid, but it cannot generate electricity without turbines, fuel, and infrastructure. The physical world imposes constraints that no amount of computation can bypass. Matter must still be mined, transported, assembled, maintained, and repaired.
This boundary defines what we might call the Analog Fortress.
The Analog Fortress is not a single industry, but a class of economic activity whose core value resides in physical scarcity, physical infrastructure, or physical accountability. These sectors do not resist AI out of technological backwardness. On the contrary, they often adopt AI aggressively. But AI serves as a force multiplier, not a substitute. It improves their efficiency without eroding their necessity.
The first layer of the Analog Fortress is physical scarcity.
In a world where digital content approaches infinite abundance, physical space and material become more valuable, not less. You cannot download a home. You cannot stream a highway. You cannot synthesize gravel inside a data center.
Companies that quarry aggregates, manufacture flooring, produce industrial gases, or refine chemicals operate within geological and industrial constraints. Their assets are tied to land, mineral deposits, and physical plants. Vulcan Materials does not compete on code velocity; it competes on ownership of quarries that took millions of years to form. Mohawk Industries does not compete on algorithmic creativity; it competes on manufacturing capacity, supply chains, and installation networks.
AI can help design better materials. It can improve logistics. But it cannot eliminate the need for atoms.
The second layer is life-support infrastructure.
AI is often described as ethereal, but its physical footprint is immense. Every AI model runs on hardware. Hardware consumes electricity. Electricity must be generated, transmitted, and distributed through physical networks. Data centers are not metaphors; they are industrial facilities consuming gigawatts of power, millions of gallons of cooling water, and vast quantities of building materials.
This transforms utilities into landlords of the AI era.
Electrical grids, nuclear plants, and energy pipelines occupy a structurally advantaged position. They provide the substrate on which AI depends. Constellation Energy, NextEra, and similar utilities do not compete with AI; they supply its metabolism. As AI adoption accelerates, demand for reliable baseload power increases. Nuclear plants, once viewed as legacy infrastructure, become strategic assets.
Waste management occupies a similar position. Consumption produces physical residue. AI may optimize production, but it does not dematerialize it. Every factory, every household, and every data center produces waste that must be collected, transported, and processed. Waste Management Inc. does not compete with software; it manages entropy in physical form.
The third layer is human accountability.
In digital systems, errors can often be reversed. A corrupted file can be restored from backup. A mispriced trade can be unwound. But in the physical world, consequences are often irreversible. When a patient undergoes surgery, when a bridge is constructed, when a naval vessel is deployed, the cost of failure is measured in lives and national security, not inconvenience.
In these domains, human presence carries legal, emotional, and institutional weight.
Healthcare providers, surgical specialists, and medical device manufacturers operate within systems that require human responsibility. AI can assist in diagnosis and planning, but patients and regulators demand accountable actors. Defense contractors operate under even stronger constraints. Companies like Huntington Ingalls build aircraft carriers and submarines whose scale, complexity, and regulatory environment create barriers measured in decades and billions of dollars.
These industries are not insulated from AI. They are reinforced by it. AI improves design, maintenance, and operational efficiency. But the underlying physical systems remain indispensable.
Taken together, these layers form the foundation of an Anti-Fragile Portfolio.
Fragility emerges when a system’s value depends on assumptions that technological progress can invalidate. Many purely digital companies face this risk. A breakthrough model can collapse the economic value of existing software by making its function trivial.
Analog Fortress companies operate under different constraints. Their value derives from physical necessity, regulatory structure, and capital intensity. These characteristics make them resistant to displacement. More importantly, they position them to benefit from AI’s expansion.
As AI scales, it increases demand for electricity, construction, transportation, cooling, and maintenance. Data centers require concrete, steel, and land. Energy consumption rises. Infrastructure expands. Each of these requirements strengthens the economic position of companies embedded in physical systems.
This dynamic reflects a deeper principle known as the Lindy Effect: the longer a system has survived, the longer it is likely to persist. Roads, electrical grids, pipelines, and industrial materials have endured through multiple technological revolutions because they address persistent physical constraints.
AI does not repeal thermodynamics. It intensifies its consequences.
This reframes how we interpret technological progress. Each wave of digitization does not eliminate the physical world. It concentrates value within it. As information becomes abundant, the bottleneck shifts to what cannot be copied.
Scarcity migrates downward, from software to infrastructure.
This inversion produces a paradox. The more advanced AI becomes, the more valuable the most primitive-seeming industries may appear. Gravel, electricity, waste removal, and medical hardware do not capture headlines. They do not produce viral demos. But they anchor the physical systems on which AI depends.
The future, in this sense, belongs not only to those who build intelligence, but to those who supply its environment.
AI is not an autonomous civilization. It is an energy conversion process expressed through computation. It consumes electricity, produces heat, and requires physical containment. It lives inside machines, inside buildings, inside grids.
It remains subject to gravity.
And gravity belongs to the Analog Fortress.