It's easy for people (especially people who like tech) to forget this. "Innovation" in the sense of better algorithms or improved computations are great, but they don't solve problems - applications do.
That's why many 'hot' areas (like machine learning, for example) have a large gap between industry and academia. Researchers focus on "innovation" in terms of new approaches, aiming for marginally higher performance, and requiring increasingly complex models/infrastructure to unlock the remaining performance. On the other hand, combining an existing technology with industry knowledge of how to apply it can have a huge impact.
