I was oversimplifying my comments a bit. There is the curse of dimensionality. And in causal inference if you just use every variable as a confounder your model can also get worse because you're blocking forward paths. But if you know what you're doing it shouldn't be a problem. And I haven't met any ML practitioner or statistician who doesn't realize the importance of getting to understand your data and making proper modelling decisions.