How is AI driven demand creating a global semiconductor capacity crunch despite TSMC simultaneously building about 20 fabs—13 in Taiwan and five to six overseas—at roughly five times its previous construction scale; what constraints, especially construction worker shortages in Ta

AI demand is straining a supply chain whose bottlenecks are no longer just leading-edge wafer fabs: advanced packaging, high-bandwidth/data-center memory, specialized equipment, utility capacity, and skilled construction and process labor all expand more slowly than AI compute demand. TSMC’s near-20-fab buildout is therefore large but not sufficient to remove the near-term crunch. 5

• Why more fabs do not immediately solve it: Building 13 fabs in Taiwan and five to six abroad distributes capacity over several years, while each AI accelerator also requires scarce leading-edge logic, CoWoS advanced packaging, and large quantities of specialized memory. Capacity must come online across all of those stages simultaneously; a shortage at packaging or memory can leave finished compute systems constrained even if wafer output rises. 5

• Expansion is labor- and infrastructure-constrained: Construction-worker shortages are a stated challenge for TSMC’s Arizona expansion. 2 In Taiwan, TSMC has also flagged shortages of skilled talent and concerns about water availability, while the concentration of simultaneous fab projects intensifies competition for construction crews, engineers, suppliers, power, and industrial land. 1

• TSMC’s productivity response: TSMC is applying AI within manufacturing to improve process control, equipment utilization, yield learning, fault detection, and factory scheduling—measures that extract more usable output from installed tools but do not substitute for new physical capacity. Its planned three-hectare Baipu Industrial Park site in Kaohsiung is intended to speed testing and qualification of advanced-packaging materials and equipment; it will include small clean rooms, laboratories, and training capacity. 6

• Why CoWoS and memory remain short: CoWoS is difficult to scale because it is a high-precision, yield-sensitive integration process linking logic and memory in AI packages. At the same time, AI data centers are consuming memory faster than suppliers can add qualified capacity, especially for high-performance products. SK leaders have warned that the broader wafer and memory shortfall could persist until around 2030 as capacity takes four to five years to build. 5

• SK Group’s potential response: SK is considering further overseas memory investment, including a possible new plant in Japan, and has left open joint production and R&D cooperation with Kioxia. These are options under review, not announced commitments. 9

• Revenue implication: A Taiwan semiconductor-revenue projection near $300 billion this year signals that scarcity is currently supporting exceptionally strong sales growth—but it also exposes Taiwan to execution risk from labor, water, energy, equipment, and packaging constraints. The upside case is substantial: SEMI has projected the global chip market could exceed $2 trillion by 2030 on AI-infrastructure demand. 10

• Important forecast caveat: The $2 trillion figure is an industry projection, not a consensus certainty. TSMC’s own earlier 2030 outlook was more than $1.5 trillion, illustrating how sensitive long-range totals are to AI demand, pricing, and the pace at which supply-chain bottlenecks are resolved. 4