Chinese companies need to pay much higher prices for the same GPUs, so they would need to charge more to make a profit, but it's difficult to charge more unless they have a much better product. So building massive data centers to gain market share is riskier for them.

That said, Alibaba not releasing the weights for Qwen3-Max and announcing $53 billion in AI infrastructure spending https://www.reuters.com/world/china/alibaba-launches-qwen3-m... suggests that they think they're now at a point where it makes sense to scale up. (The Reuters article mentions data centers in several countries, which I assume also helps work around high GPU prices in China.)

Circling back to OpenAI: I don't think they're spending so much on infrastructure just because they want to train bigger models on more data, but moreso because they want to serve those bigger models to more customers using their services more intensively.

Most likely OpenAI has models at least as efficient as DeepSeek or Qwen. Cerebras offers both GPT-OSS-120B and Qwen3-235B-Instruct. Obviously, the second has twice as many parameters as the first, but that's the closest comparison I can find. The Qwen model is twice as large, but twice as slow (1400 tokens/second vs 3000) and 50% more expensive ($1.2 per million tokens vs $0.75). Now, OpenAI is running a proprietary model, and most likely it is much optimized than the free version they release for public use.

[1] https://inference-docs.cerebras.ai/models/overview

Because they have more distribution than anyone else? Pretty much everyone uses chat.com but almost no-body uses deepseek.

They dont, your question is just as wrong as asking, "Why does LockheedMartin need so many material science engineers? Chengdu Aircraft makes fighters with 10x fewer"

They’re trying to lock out competition from accessing compute.