The best, in fact the only way to generate truly convincing text output on most subjects is to understand, on some level, what you're writing about. In other words, to create a higher level abstraction than simply "statistically speaking, this word seems to follow that one". Once you start to encode that words map to concepts, you can use the resulting conceptual model to create output which is conceptually consistent, then map it backwards to words. There is what humans do with sensory data, and there is good evidence that GPT-3 is doing this too, to some degree.
Take simple arithmetic, such as adding two and three digit numbers. GPT-2 could not do this very successfully. It did indeed look like it was treating it as a "find the textual pattern" problem.
But GPT-3 is much more successful, including at giving correct answers to arithmetic problems that weren't in its training set.
So what changed? We aren't sure, but the speculation is that in the process of training, GPT-3 found that the best strategy to correctly predicting the continuation of arithmetic expressions was to figure out the rules of basic arithmetic and encode them in some portion of its neural network, then apply them whenever the prompt suggested to do so.
If this is the case, and it remains speculation at this point, would you still argue that GPT-3 doesn't "understand" arithmetic, on some level? I would argue that this abstraction, this mapping of words onto higher-level concepts, which can then be manipulated to solve more complex problems, is exactly what intelligence is, once you strip away biologically-biased assumptions.
Certainly, at this point GPT-3's conceptual understanding remains somewhat primitive and unstable, but the fact that it exhibits it at all, and sometimes in spookily impressive ways, is what has people excited and worried. We have produced AIs that can perhaps think conceptually about relatively narrow topics like playing Go, but we have never before created one that can do so one such a wide range of topics. And there is no suggestion that GPT-3's level of ability represents a maximum. GPT-4 and beyond will be more powerful, meaning that it can mine more and more powerful conceptual understanding from their training data.