It is actually pretty straightforward why those model "reason" or, to be more exact, can operate on a complex concepts. By processing huge amount of texts they build an internal representation where those concepts are represented as a simple nodes (neurons or groups). So they really distill knowledge. Alternatively you can think about it as a very good principal component analysis that can extract many important aspects. Or like a semantic graph built automatically.
Once knowledge is distilled you can build on top of it easily by merging concepts for example.
Once knowledge is distilled you can build on top of it easily by merging concepts for example.
So no secret here.