TechCheck U.S., China and the race for cheaper AI 11/10/25
Nov 10, 2025
The AI landscape is heating up with news of an $18 billion financing deal tied to Oracle. A striking comparison unfolds as the U.S. invests heavily in large data centers, while China adopts a leaner, cost-effective approach. Notably, Chinese models like Kimmy and Alibaba's Quen are closing the performance gap with U.S. counterparts. With a staggering spending disparity identified, insights reveal China's energy advantages and the power of open-source techniques to lower training costs. The race for cheaper AI continues to intensify!
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Two Contrasting AI Strategies
- U.S. AI expansion leans heavily on massive debt-financed data centers while China focuses on leaner, efficient deployment.
- Chinese open models and cheaper chips are closing performance gaps despite far smaller capital outlays.
High Performance, Low Cost Models
- Chinese startups like Moonshot train competitive models at a tiny fraction of U.S. costs using open-source and distillation techniques.
- Kimmy K2 reportedly trained for under $5 million yet ranks alongside top American models on benchmarks.
Capital Spending Gap Is Massive
- Capital spending projections show a huge gap: U.S. cloud giants ~ $700B vs. China's top players ~ $35–80B by 2027.
- That implies a 10–20x capex gap while model performance appears roughly similar on benchmarks.