The progression from early neural networks to today's massive models is fundamentally driven by the exponential increase in available computational power, from the initial move to GPUs to today's million-fold increases in training capacity on a single model.

Building software traditionally required minimal capital. However, advanced AI development introduces high compute costs, with users reporting spending hundreds on a single project. This trend could re-erect financial barriers to entry in software, making it a capital-intensive endeavor similar to hardware.

The focus in AI has evolved from rapid software capability gains to the physical constraints of its adoption. The demand for compute power is expected to significantly outstrip supply, making infrastructure—not algorithms—the defining bottleneck for future growth.

For decades, tech innovation was engineering-bound, as hiring more engineers didn't linearly increase output (the 'Mythical Man-Month'). AI flips this paradigm. A small team can now productively deploy massive amounts of capital on compute, shifting the primary constraint from engineering talent to capital availability.

While the world focused on GPU shortages, the real constraint on AI compute is now physical infrastructure. The bottleneck has moved to accessing power, building data centers, and finding specialized labor like electricians and acquiring basic materials like structural steel. Merely acquiring chips is no longer enough to scale.

Historically, software engineering required minimal capital—a laptop and internet. AI development now mirrors heavy industry, where the capital asset (like a $10M crane or $100M cargo ship) costs far more than the skilled operator. An engineer's compute budget can now dwarf their salary, changing team economics.

Every layer of the AI supply chain is constrained, from energy and data centers to turbines, transformers, and rare earth minerals. This is a shift from software limitations to hard physical constraints. As a result, the price of intelligence may stop decreasing and could even rise.

Unlike railroads or telecom, where infrastructure lasts for decades, the core of AI infrastructure—semiconductor chips—becomes obsolete every 3-4 years. This creates a cycle of massive, recurring capital expenditure to maintain data centers, fundamentally changing the long-term ROI calculation for the AI arms race.

Unlike durable infrastructure like railways or fiber optic cables, AI's core component—expensive GPUs—becomes obsolete in just 2-3 years. This creates a permanent, recurring cost, a 'tax on innovation,' making profitability much harder to achieve compared to previous tech revolutions.