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Author Chris Miller explains that the further down the supply chain you go (from hyperscalers to fabs like TSMC to equipment makers like ASML), the more skepticism there is about the true scale of AI demand. This "bullwhip effect" results in cautious capital expenditure, creating a manufacturing bottleneck for the AI industry.

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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.

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.

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.

Unlike the dot-com era's debt-fueled fiber overbuild, the current AI boom is constrained by wafer supply, controlled primarily by TSMC. Their disciplined capacity expansion, despite immense demand, prevents a speculative oversupply of GPUs, effectively acting as the single most important governor against an AI bubble.

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.

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.

The long-term ability to scale AI compute is not constrained by power or data centers, but by the production of advanced semiconductors. The ultimate chokepoint is ASML, the world's only manufacturer of EUV lithography tools, which can only produce just over 100 units annually by 2030.

Today's DRAM shortage stems from the post-COVID downturn. Expecting weak demand, memory producers became conservative with capital expenditures and didn't expand capacity. This left the industry unprepared for the sudden, explosive demand for memory driven by the AI boom.