>ML is mainly just going to become automated p-hacking, and bring about a dark age to much of science

It is already happening. A lot of people who can contribute to actual Science are moving into AI/ML field for the money and the industry/media hype are reinforcing this. Everything is "Deep${NONSENSE}" nowadays whether it is relevant or not. As a beginner, when i started to learn NNs, i couldn't get past my initial hurdle on how to validate the results on actual real-world data. What Statistical metrics do i use to "know" that the blackbox is working correctly? What are the assumptions and limitations that i need to be aware of to understand and have faith in the output? Most people don't seem to know or care; it is "magic" to them. In a world awash with data, reckless application of NN models to any and every problem is only going to drown us in spurious results and muddying all Scientific endeavours.