This is an interesting article if you read it like a howto for constructing a neural network for performing a practical task. But if you take it at face-value, and follow a similar method the next time you need to parse some input, then, well, I don't know what to say really.

The author takes a hard problem (parsing arbitrary input for loosely-defined patterns), and correctly argues that this is likely to produce hard-to-read 'spaghetti' code.

They then suggest replacing that with code that is so hard to read that there is still active research into how it works, (i.e a neural net).

Don't over-index something that's inscrutable versus something that you can understand but is 'ugly'. Sometimes, _maybe_, a ML model is what you want for a task. But a lot of the time, something that you can read and see why it's doing what it's doing, even if that takes some effort, is better than something that's impossible.