So there are a few things to consider here. What exactly does it mean to "best humans"? In some cases, we can train on data from professional or highly skilled humans, and use that to outperform the average human.

In other situations, we can train an ML model to be a sort-of average of a lot of humans, getting rid of a lot of their individual errors and thus performing better than the average.

In other situations, we may have access to data that is not directly generated by humans, or it's made by humans with access to more data than what the model has access to. For example, to train a vision based ML system for autonomous driving, you could use ground truth data obtained by other means (e.g. LIDAR).

Another sort-of similar situation is training an ML model to predict the stock market. Here, you can use actual stock market trends as the ground truth, rather than the guesses of humans.

So all in all, there are many situations where ML models aren't limited to human capabilities.