Most data is crap. So the mountains of data that are supposedly an advantage, in China or in the vaults of the large corporations, are not fit for purpose without a tremendous amount of data pre-processing, and even then... And that means the real chokepoint is data science talent, not data quantity. In other words, in many cases, the premises of this statement should be questioned.
Secondly, a lot of research is focused on few-shot, one-shot or no-shot learning. That is, the AI industry will make this constraint increasingly obsolete.
Thirdly, with synthetic data, it is only as good as the assumptions you made while creating it. But how did you reach those assumptions? By what argument should they be treated as a source of truth? If you are making those assumptions based on your wide experience with real-world data, well then, we run into the same scarcity, mediated by the mind of the person creating the synthetic data.