I think the best way to answer your question is to ask you to be more precise about what, exactly, you mean by "outperform".

There's some limited sense in which your reasoning works as you seem to have envisioned. A generative model like GPT or GANs is typically built at least partly to produce output that's indistinguishable from what is produced by a human, using some kind of autoregressive data set or adversarial objective. By definition, it cannot do better at that goal, because a human has a 100% success rate, by definition, at producing something indistinguishable from what is produced by a human.

But there are limitations to this reasoning:

1. Producing any arbitrary human-like output is not actually the goal. People don't evaluate generative models on how human-like they are, but rather on how useful their results are. There are lots of ways their results can be more useful even if they aren't quite as "human-like". In fact, the motivation for trying to keep the results human-like is mainly that allowing a generative model too much freedom to generate samples that are very different from its training set decreases accuracy, not that it's a goal in its own right.
2. That's not all of machine learning anyway. Another very common task is, for example, Netflix predicting what movies you will want to watch to build their recommendations. Humans are involved in producing that data, but it's not learning from data about what other humans predicted users would watch. It's learning directly from observed data about what humans really did watch. Such a system isn't aiming to emulate humans at all. Some machine learning is even trained on data that's not generated by humans at all, but rather the objective it's training to optimize is either directly observed and measured, or directly computed.
3. Even in cases where a supervised model is learning to predict human labeling, which is where your reasoning best applies, the quantity of data can overcome human accuracy. Imagine this simpler scenario: I am learning to predict which President is on a U.S. bill, given the denomination amount. This is an extremely simple function to learn, of course, but let's say I only have access to data with a rather poor accuracy rate of 60%, with errors occurring uniformly. Well, with enough of that data, I can still learn to be 100% accurate, simply by noting which answer is the most common for each input! That's only a theoretical argument, and in a realistic ML context it's very difficult to get better-than-human performance on a supervised human-labeled task like this. But it's not impossible.
4. And, of course, if you look at more than just accuracy, ML can be "better" than humans in many ways. They can be cheaper, faster, more easily accessible, more deterministic, etc.