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.
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Reports of China building a working EUV lithography machine are misleading. The effort appears to be an assembly of smuggled components from ASML's existing supply chain, not a story of domestic innovation. This frames the primary challenge as one of export control evasion rather than a rapid technological leap by China.
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.
Contrary to the narrative of a close race, Huawei's AI chips are falling further behind NVIDIA's. The performance gap is projected to widen from a 5x difference to a 17x difference within two years. Shockingly, Huawei's next-generation chip is actually projected to be less powerful than its current leading model, indicating significant production struggles.
Contrary to the narrative of a simple "tech race," the assessment is that China is already ahead in physical AI and supply chain capabilities. The expert warns that this gap is not only expected to last three to five years but may widen at an accelerating rate, posing a significant long-term competitive challenge for the U.S.
America's competitive AI advantage over China is not uniform. While the lead in AI models is narrow (approx. 6 months), it widens significantly at lower levels of the tech stack—to about two years for chips and as much as five years for the critical semiconductor manufacturing equipment.
China's semiconductor strategy is not merely to reverse-engineer Western technology like ASML's. It's a well-funded "primacy race" to develop novel, AI-driven lithography systems. This approach aims to create superior, not just parallel, manufacturing capabilities to gain global economic leverage.
China is compensating for its deficit in cutting-edge semiconductors by pursuing an asymmetric strategy. It focuses on massive 'superclusters' of less advanced domestic chips and creating hyper-efficient, open-source AI models. This approach prioritizes widespread, low-cost adoption over chasing the absolute peak of performance like the US.
While the West may lead in AI models, China's key strategic advantage is its ability to 'embody' AI in hardware. Decades of de-industrialization in the U.S. have left a gap, while China's manufacturing dominance allows it to integrate AI into cars, drones, and robots at a scale the West cannot currently match.
