I partly agree with this but I think it's a bit overrated. There's no massive barrier to getting an actually good model into production. If the model seems promising but no one can figure out how to productionize it, it probably has fatal flaws as a model. There's no guarantee that a mess of Python code that somehow produces a nice AUC curve is actually doing anything valuable. As in science in general, there are many ways to fool yourself in data science.