Analyst Chris Miller argues China's core challenge is manufacturing, as it lacks the advanced lithography tools monopolized by ASML. The US and Taiwan are projected to produce 30 times more quality-adjusted AI chips, a gap unlikely to close soon.

While AI model providers may overstate demand, the most telling signal comes from TSMC. Their decision to significantly increase capital expenditure on new fabs, a multi-year and irreversible commitment, indicates a strong, cynical belief in the long-term reality of AI compute demand.

AI software models advance every few months, creating exponential demand. However, the hardware infrastructure like chip fabs operates on two-to-four-year development cycles. This timeline disconnect between software's rapid pace and hardware's slow build-out creates a persistent supply crunch that money alone cannot instantly solve.

Despite huge demand for AI chips, TSMC's conservative CapEx strategy, driven by fear of a demand downturn, is creating a critical silicon supply shortage. This is causing AI companies to forego immediate revenue.

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.

While energy supply is a concern, the primary constraint for the AI buildout may be semiconductor fabrication. TSMC, the leading manufacturer, is hesitant to build new fabs to meet the massive demand from hyperscalers, creating a significant bottleneck that could slow down the entire industry.

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.

While energy is a concern, the highly consolidated semiconductor supply chain, with TSMC controlling 90% of advanced nodes and relying on a single EUV machine supplier (ASML), creates a more immediate and inelastic bottleneck for AI hardware expansion than energy production.