The demand for HBM memory for AI is causing a global shortage because of a ~4:1 manufacturing trade-off: each bit of HBM produced consumes capacity that could have made four bits of standard DRAM. This supply crunch will raise prices for all electronics, from phones to PCs.

The growth of AI is constrained not by chip design but by inputs like energy and High Bandwidth Memory (HBM). This shifts power to component suppliers and energy providers, allowing them to gain leverage, demand equity, and influence the entire AI ecosystem, much like a central bank controls money.

Contrary to the long-term belief that AI will be deflationary, the current surge in demand for computer equipment for data centers is stronger than supply, causing prices to spike and contributing significantly to producer price inflation (PPI).

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.

While AI may be deflationary in the long run, its immediate effect is inflationary. The immense capital expenditure on data centers, hardware, and energy strains supply chains, creates electricity shortages, and drives up prices for physical goods and skilled labor. Policymakers should focus on this immediate pressure, not on speculative future deflation.

Electricity prices have been on a consistent upward climb, contributing to inflation that directly impacts household budgets. A key driver behind this trend is the massive and growing energy demand from AI data centers. This suggests a new, structural source of upward pressure on utility costs that is just beginning.

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.

The primary constraint for AI giants like OpenAI and Anthropic is not the supply of chips, but the availability of electrical power and grid infrastructure for data centers. This fundamental chokepoint shifts the strategic advantage to hyperscalers who already control massive power and infrastructure assets.

Even if NVIDIA and TSMC solve wafer shortages, the AI industry faces a looming energy (watt) bottleneck. The inability to power new data centers could cap AI growth, shifting the primary constraint from semiconductor manufacturing to energy infrastructure and supply.