The ongoing AI semiconductor shortage is not a temporary supply shock, but a deepening structural problem with a bottleneck that is constantly shifting and compounding.
Initially, the primary constraint was in advanced packaging. Specifically, TSMC's CoWoS technology, essential for assembling high-performance AI chips, couldn't keep up with demand. This was evident when NVIDIA reportedly booked a significant portion of TSMC's CoWoS capacity well into 2026, creating the first major chokepoint in the AI supply chain.
However, the problem has since moved. The new, and perhaps more challenging, bottleneck is data center infrastructure, particularly power and physical space. As companies race to build massive AI data centers, they are running into fundamental limits. For instance, Micron noted its new Singapore fab alone could require up to 500 transformers, highlighting a severe shortage in heavy electrical equipment. Gartner has projected that by 2027, 40% of AI data centers will face power restrictions, turning grid capacity into the critical path for AI expansion.
Now, a third bottleneck is emerging: a shortage of leading-edge wafer capacity. This is driven by two key factors. First, the balance of power at foundries has shifted. NVIDIA is set to overtake Apple as TSMC's largest customer, symbolizing the prioritization of AI/HPC chips over smartphones for the most advanced manufacturing processes. Second, the production of HBM (High Bandwidth Memory) is having a significant impact. HBM requires 3-4 times more wafer area per bit compared to standard DRAM. As memory makers dedicate more production lines to HBM, the overall supply of general-purpose DRAM shrinks, a structural change that SK Group's chairman warned could last until 2030.
This entire cycle is fueled by soaring demand from increasingly complex AI models, particularly those used for reasoning and agent-based tasks, which consume vast amounts of tokens. To address the resulting power and bandwidth challenges, the industry is actively developing next-generation solutions like CPO (Co-Packaged Optics), which aims to improve efficiency by integrating optical connections directly with silicon.
Despite these long-term efforts, a significant risk looms over the market: double ordering. Fear of shortages often leads companies to place multiple orders for the same components, artificially inflating demand. If real demand softens, these excess orders could be canceled abruptly, leading to a sharp inventory correction and price collapse, a pattern seen in previous semiconductor cycles.
- CoWoS (Chip-on-Wafer-on-Substrate): An advanced 2.5D packaging technology developed by TSMC that stacks chips together to increase performance and save space, crucial for high-end AI accelerators.
- HBM (High Bandwidth Memory): A type of high-performance RAM that uses stacked memory chips to provide a much wider data bus than traditional DRAM, essential for feeding data to powerful GPUs.
- CPO (Co-Packaged Optics): A technology that places optical connectivity components very close to the main processor chips within the same package, designed to reduce power consumption and increase data transfer speeds inside data centers.