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.

The current AI moment is unique because demand outstrips supply so dramatically that even previous-generation chips and models remain valuable. They are perfectly suited for running smaller models for simpler, high-volume applications like voice transcription, creating a broad-based boom across the entire hardware and model stack.

The AI industry's growth constraint is a swinging pendulum. While power and data center space are the current bottlenecks (2024-25), the energy supply chain is diverse. By 2027, the bottleneck will revert to semiconductor manufacturing, as leading-edge fab capacity (e.g., TSMC, HBM memory) is highly concentrated and takes years to expand.

The primary constraint on AI scaling isn't just semiconductor fabrication capacity. It's a series of dependent bottlenecks, from TSMC's fabs to the limited number of EUV machines from ASML, and even further down to ASML's own specialized suppliers for components like lenses and glass.

Despite soaring AI demand, chip fab TSMC is conservatively expanding capacity. This is a rational move to avoid the catastrophic downside of overcapacity, where fixed costs sink profitability for years. However, this decision is creating a massive, predictable chip shortage for the AI industry.

Unlike standard DRAM where products are standardized, HBM is less of a commodity. The complexity of manufacturing HBM—stacking multiple dice and advanced packaging—allows suppliers to differentiate on technology, yield, and thermal performance, giving them a competitive edge beyond just price.

Producing specialized High-Bandwidth Memory (HBM) for AI is wafer-intensive, yielding only a third of the memory bits per wafer compared to standard DRAM. As makers shift capacity to profitable HBM, they directly reduce the supply available for consumer electronics, creating a severe shortage.

In a surprising market inversion, the price surge for commodity DRAM has become so extreme that its profit margins now exceed those of specialized High-Bandwidth Memory (HBM). This creates a strategic dilemma for producers, forcing them to balance short-term profits against long-term AI market position.

Despite record profits driven by AI demand for High-Bandwidth Memory, chip makers are maintaining a "conservative investment approach" and not rapidly expanding capacity. This strategic restraint keeps prices for critical components high, maximizing their profitability and effectively controlling the pace of the entire AI hardware industry.