Your experience rings true to me. It's one of my biggest frustrations with ML at the moment. There are so many ideas I'd like to try, but I know only a small fraction of them will work, and discovering which of the ideas fail and which succeed is a herculean task. Your conclusion that empathy helps may also be true, but I have a different take.

It currently takes way too much time to explore ML-based ideas. I compare this to the early days of computer programming where programmers needed to manually fill out punch cards, and doing anything took days of full time work. There is lots of room for improvement along every step of the ML pipeline, from data wrangling, model choice, training, and evaluation. Good ML tooling will likely bring huge gains in the field.