why is all dev tool innovation in the AI/ML space focused on the least time consuming stuff?

Every DS, DE, and MLE I speak to (including myself) spends a majority of their time focused on data prep. Even though this is taking up 70-90% of everyone's time all the investment and innovation focus seems to be on training, fine-tuning, and on-demand inference. Why? Is data prep not sexy?

It is imperative that high quality samples are being used for training, shit data is going to create a shit model. I feel like there should be more focus …

data data prep datascience dev every fine-tuning focus innovation investment least mle space speak tool training