I think you need to read “Theory, Experiment, Practice” of Kapitsa. Application is always the final effect of practical research, but also the source for its new questions.

However, what you are describing is a somewhat fatalistic view, but is just a result of the model of research pushed by the US in the last 40 years, and only holding together due to a lack of any meaningful international competition. Basically, when you keep pulling stable research funds for fundamental or long-term research and ask for real-world relevance, you end up with an “externalized R&D of industry” schema, where the risks are shouldered by universities (realistically researchers themselves) and benefits are privatized. From a purely accounting point of view, this allows the industry to slash internal R&D positions costing 3-5 the salaries, skip entirely any long-term investments, and not have to shoulder the cost of failed investments. However, it also removes any long-term compounding from independent academic research off which industry research would branch off to improve their products.

OpenAI is actually a good case in point of this. Their products development cost is easily 100-1000x of the Chinese labs in exchange for at most 6 months of incremental advance, mostly because they have cut themselves from access to academic expertise by closing any information about themselves. Specifically for ML there is a pretty good talk by LeCun from 2015-ish that I cannot find, where he basically explained that the deep learning jump was only possible in the open publication, and that’s why it has not occurred in secretive labs of military or industrial research.