The global semiconductor industry is falling into a dangerous illusion: that building more fabs alone can meet the explosive demand for AI compute. This belief has fueled an unprecedented wave of capital expenditure—over $180 billion was invested globally in new 12-inch wafer fabs in 2025 alone. Yet beneath this surge in manufacturing capacity lies a shifting bottleneck, migrating from fabrication to design, advanced packaging, and ecosystem integration—areas where investment remains critically underfunded.
Take NVIDIA as a case in point. Its Blackwell GPUs rely on TSMC’s 3nm process, but even with TSMC allocating roughly 70% of its 3nm capacity to NVIDIA, supply still lags behind orders. The constraint isn’t wafer output—it’s advanced packaging. CoWoS (Chip-on-Wafer-on-Substrate) has become essential for AI accelerators, yet TSMC remains virtually the only supplier capable of high-volume, high-yield CoWoS production. In Q2 2025, TSMC’s monthly CoWoS capacity stood at approximately 18,000 panels, while total market demand exceeded 30,000. This gap cannot be closed by simply constructing new fabs. As Lam Research CEO Tim Archer bluntly stated, “New fabs alone will not solve chip bottlenecks.”
A deeper vulnerability lies in the concentration of the design ecosystem. Over 90% of AI accelerators are built on Arm architectures, and electronic design automation (EDA) tools are dominated by just three U.S. firms: Synopsys, Cadence, and Siemens EDA. This “soft monopoly” is less visible than equipment export controls but equally constraining. When countries like Malaysia and Vietnam push to build regional chip design capabilities—as Malaysia is doing through its Southeast Asia design alliance—they quickly discover that talent is only part of the problem. What’s missing is a mature IP licensing framework, verification infrastructure, and software co-optimization pipelines. Despite policy support, no Southeast Asian hub has yet emerged as a credible alternative to design clusters in Silicon Valley or Taiwan, China.
Meanwhile, AI server demand is triggering supply chain fragmentation. The recent ASIC partnership between Anthropic and Microsoft could accelerate a shift among cloud providers away from general-purpose GPUs toward custom silicon. If this trend broadens, it will force memory giants like Samsung and SK Hynix to recalibrate their roadmaps. NVIDIA’s new Vera CPU platform, for instance, demands higher bandwidth from LPDDR5X memory—a development that directly benefits Samsung and SK Hynix’s mobile DRAM segments. But it also signals that future chip competition won’t hinge solely on transistor density. Instead, system-level integration—from CPUs and GPUs to HBM, power management ICs, and even thermal control chips like Weltrend’s fan motor drivers—will determine real-world performance.
I judge that 2026–2027 will serve as a stress test for the AI chip industry. The ramp-up of manufacturing capacity will collide with limitations in design iteration speed, packaging yield, and software stack compatibility. Nations and companies betting exclusively on fab construction may find themselves sitting on idle capacity, unable to deliver complete solutions. True competitive advantage will belong to those who can close the loop across manufacturing, design, packaging, and software.
Alarmingly, current semiconductor policies worldwide remain fixated on “manufacturing reshoring” while neglecting the digital infrastructure of chip development—EDA tools, reusable IP libraries, verification platforms, and open-source hardware frameworks. South Korean industry leaders have already raised concerns about excessive concentration among a few dominant players, warning of heightened monopolization risks. As AI chips evolve from scarce components into integrated system capabilities, the decisive battlefield may shift from cleanrooms to developers’ workstations. Over the next five years, the entity that builds the most efficient, open, and scalable chip development ecosystem—not just the largest fab—will hold the key to the AI era.