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.

While the US currently leads in AI with superior chips, China's state-controlled power grid is growing 10x faster and can be directed towards AI data centers. This creates a scenario where if AGI is a short-term race, the US wins. If it's a long-term build-out, China's superior energy infrastructure could be the deciding factor.

Contrary to the theory that a nation could achieve AGI by using vast amounts of cheap energy to power older chips, evidence shows this is not viable. All frontier models to date have been trained on the most advanced semiconductor nodes (5nm or less), indicating that architectural efficiency is a non-negotiable requirement.

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 most dangerous policy mistake would be reverting to a 'sliding scale' that allows China to buy chips that are a few generations behind the cutting edge. In the current era of AI, performance is aggregatable. China could simply purchase massive quantities of these slightly older chips to achieve compute power equivalent to frontier systems.

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.

China can compensate for less energy-efficient domestic AI chips by utilizing its vast and rapidly expanding power grid. Since the primary trade-off for lower-end chips is energy efficiency, China's ability to absorb higher energy costs allows it to scale large model training despite semiconductor limitations.

Beyond the well-known semiconductor race, the AI competition is shifting to energy. China's massive, cheaper electricity production is a significant, often overlooked strategic advantage. This redefines the AI landscape, suggesting that superiority in atoms (energy) may become as crucial as superiority in bytes (algorithms and chips).

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.