When you select factors non-systematically, having too many factors is detrimental, which increases your likelihood of overfitting.
When more factors come from simply screening with simple and systematic methods (e.g., A.Y. Chen) or systematically randomly generated (e.g., Bryan Kelly), then more factors precisely reduces the risk of uncertainty, as Yuval's example above.