[HarHarVeryFunny]

The way you make an LLM smarter is by training it on more data, which means you need to make it bigger, which means it costs more to train.

High quality data is expensive. Synthetic data will get you so far, but after that you need to start paying experts to create data for you, which has been going on for a long time. The latest thing is paying for human written LLM-as-judge AI-output evaluation "rubrics", trying to extend RLVR into areas where "looks like it checks the boxes" is the best you can do.

When anyone, Chinese or not (Elon Musk cheerfully admits to distilling OpenAI models) uses the output of someone else's model to train their own, then what they are primarily getting is cheap training data, but you still need to train your model on this data! You may have reduced the cost/speed of training data acquisition, but if you are training a 3T param model (Kimi 3) then you still need the compute to do that - that did not change.

There was an interesting mention of the cost of training data in the recently leaked DeepSeek investor meeting, where their CEO referred to the cost of human-generated training data in China (i.e. using Chinese labor) as being the same as that in the US, which seems surprising. He also mentioned the time such data takes to be created. No doubt the Chinese will catch up in this area - this is just time and money, not Dutch technology (ASML) that the US is blocking them from buying.