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US AI folk were leading for two years by just throwing more and more compute at the same thing that Google threw them like a bone years ago (namely transformers). They made next to no innovation in any area other than how to connect more compute together. The idea of additional inference time compute, looping the network back on its own outputs, which is the only significant conceptual advancement of last years was something I, as a layman, came up with after few days of thinking why AI sucks and what can be done to make it able to tackle problems that require iterative reasoning. They announced it few weeks after I came up with the idea, so it was in the works for some time, but it shows you how basic idea it was. There was nothing else.
Suddenly when there comes a small company that introduced few actual algorithmic advancements which resulted in 100x optimization which is something expected with algorithmic optimizations, the big AI suddenly went into full "dog ate my homework" mode. Blaming everyone and everything around.
Let's not mention the fact that if full outputs of their models could enable them to train a better model at 1% cost then it puts them in even worse light that they didn't do it.