If what you say is true, and distilling LLMs is easy and cheap, and pushing the SOTA without a better model to rely on is dang hard and expensive, then that means the economics of LLM development might not be attractive to investors - spending billions to have your competitors come out with products that are 99% as good, and cost them pennies to train, does not sound like a good business strategy.
AFAIK DeepSeek have not publicly acknowledged training their model on OpenAI output - the OpenAI people have alleged that they did.
At any rate, I don't think distillation involves 'slurping out' the whole model, as I understand it, it means providing the other model's output as training data input to create your new model. Maybe analogous to an expert teaching a novice how to do something by providing carefully selected examples, without having to expose the novice to all the blind alleys the expert went down to achieve mastery.
