In reply to JJ.

An excellent question! I was hoping that someone would notice that. The x1 variable is uniform on [0,1], so it is always positive. In contrast, the x2 variable is normally distributed, which means it is (approximately) centered. The fact that x1 is not centered means that it is more correlated with the intercept. If you change the definition of x1 to use normal data, then the correlations with the intercept are approximately the same magnitude. If you center the x1 data (uniform - 0.5), then the correlation between x1 and the intercept is not as strong.