Ben Thompson argues that while investing in unproven fabs from Intel or Samsung seems risky, the greater risk is the entire AI industry being constrained by TSMC's singular capacity. The future opportunity cost of foregone revenue from this bottleneck far outweighs the expense of building up viable competitors.
Related Insights
For companies like NVIDIA or Google, moving from TSMC to Intel or Samsung is not a simple supplier switch. It necessitates a complete redesign of the chip's architecture to fit the new foundry's technology. This complex and costly process can take one to two years, making it a last resort.
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.
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.
The critical constraint on AI and future computing is not energy consumption but access to leading-edge semiconductor fabrication capacity. With data centers already consuming over 50% of advanced fab output, consumer hardware like gaming PCs will be priced out, accelerating a fundamental shift where personal devices become mere terminals for cloud-based workloads.
OpenAI's compute deal with Cerebras, alongside deals with AMD and Nvidia, shows that hyperscalers are aggressively diversifying their AI chip supply. This creates a massive opportunity for smaller, specialized silicon teams, heralding a new competitive era reminiscent of the PC wars.
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.
Despite record capital spending, TSMC's new facilities won't alleviate current AI chip supply constraints. This massive investment is for future demand (2027-2028 and beyond), forcing the company to optimize existing factories for short-term needs, highlighting the industry's long lead times.