It's an interesting metaphor, but the math is not directly applicable to software engineering, because the shape of the problem (not to mention the terms) doesn't translate.

The main problem is that shannon-nyquist is concerned with discrete representation of a continuous/analog signal. But TA is applying it backwards: software is discrete and human understanding is analog. So human "sampling" of software is not something an A->D theory can speak to.

I'm not sure that's right. The "changes in tech" can be regarded as almost continuous (in the same way as a gas is made up of discrete particles, but can still be treated as a continuous medium because there are so many particles).

And human understanding of tech is discrete - nobody understands "software", say. They understand a number of individual features of individual languages, and some of the ways those features work together.

That doesn't necessarily mean that it's a good analogy. But I can kind of see it in, say, the new versions of C++. Many people can't or don't learn all the implications of all the changes before the next version comes out.