AI cannot solve 'Baumol's disease'—the stagnant productivity in labor-intensive services like plumbing and electrical work. In fact, the AI build-out worsens it by consuming scarce skilled labor for data center construction and maintenance, driving up costs for these essential services for the rest of the economy.

The AI boom is causing a tangible 'crowding out' effect in the real economy. Fed President Schmid confirms hearing 'every day' from businesses that the data center build-out is creating intense competition for physical commodities like steel and copper, as well as for labor and equipment, directly impacting other industrial sectors.

The huge scale of AI data center construction, requiring thousands of skilled laborers in one location, creates a 'crowding out' effect. Local businesses in places like Abilene, Texas, cannot compete for labor like HVAC technicians, leading to shortages and potential inflationary pressures on regional economies.

In a radical attempt to address the drastic AI compute shortage, major housing developers like PulteGroup are testing the installation of micro data centers on newly built homes. These units would function as nodes in a distributed computing cluster, highlighting that every possible avenue is being explored for more compute power.

The AI industry's explosive growth has outpaced the physical infrastructure supporting it. Data centers, which follow slow real estate development cycles of permits and construction, could not be built fast enough to meet the sudden, massive demand for compute, creating a global bottleneck.

The insatiable demand for data centers is creating an upstream bottleneck: access to power. With grid connections backlogged for years, the most valuable asset is becoming 'powered land'—parcels where developers can bring their own power sources, creating a new and crucial real estate sub-market.

The massive capital rush into AI infrastructure mirrors past tech cycles where excess capacity was built, leading to unprofitable projects. While large tech firms can absorb losses, the standalone projects and their supplier ecosystems (power, materials) are at risk if anticipated demand doesn't materialize.

Contrary to popular belief, the primary constraint on expanding AI infrastructure isn't GPU supply. It's the physical world: acquiring land, getting permits, and finding enough skilled tradesmen for construction and wiring. The GPUs are one of the last items to be installed in a long, labor-intensive process.

While supply chains for GPUs and power have been major hurdles, the current primary constraint for building new data centers is a shortage of skilled construction workers. There simply are not enough electricians and laborers to build facilities quickly enough to meet demand.