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.
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New AI models are designed to perform well on available, dominant hardware like NVIDIA's GPUs. This creates a self-reinforcing cycle where the incumbent hardware dictates which model architectures succeed, making it difficult for superior but incompatible chip designs to gain traction.
The competitive landscape for AI chips is not a crowded field but a battle between two primary forces: NVIDIA’s integrated system (hardware, software, networking) and Google's TPU. Other players like AMD and Broadcom are effectively a combined secondary challenger offering an open alternative.
Nvidia dominates AI because its GPU architecture was perfect for the new, highly parallel workload of AI training. Market leadership isn't just about having the best chip, but about having the right architecture at the moment a new dominant computing task emerges.
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.
Google successfully trained its top model, Gemini 3 Pro, on its own TPUs, proving a viable alternative to NVIDIA's chips. However, because Google doesn't sell these TPUs, NVIDIA retains its monopoly pricing power over every other company in the market.
The real long-term threat to NVIDIA's dominance may not be a known competitor but a black swan: Huawei. Leveraging non-public lithography and massive state investment, Huawei could surprise the market within 2-3 years by producing high-volume, low-cost, specialized AI chips, fundamentally altering the competitive landscape.
China's refusal to buy NVIDIA's export-compliant H20 chips is a strategic decision, not just a reaction to lower quality. It stems from concerns about embedded backdoors (like remote shutdown) and growing confidence in domestic options like Huawei's Ascend chips, signaling a decisive push for a self-reliant tech stack.
Hyperscalers face a strategic challenge: building massive data centers with current chips (e.g., H100) risks rapid depreciation as far more efficient chips (e.g., GB200) are imminent. This creates a 'pause' as they balance fulfilling current demand against future-proofing their costly infrastructure.
Attempts to undermine Chinese chip maker Huawei by allowing NVIDIA to sell chips to China are flawed. The Chinese government operates outside typical market dynamics and will ensure unlimited demand for Huawei's products, making NVIDIA a temporary gap-filler that inadvertently turbocharges China's AI industry.
China's superior ability to rapidly build energy infrastructure and data centers means it could have outpaced US firms in building massive AI training facilities. Export controls are the primary reason Chinese hyperscalers haven't matched the massive capital spending of their US counterparts.