Developers and machines that can't explain themselves end up out on their ear or damaging the projects they're involved in.

But, as the article points out, at least for a considerable set of problem domains, it's not.

My general rule of thumb is that anything that has a relatively small, finite, discrete set of inputs and outputs is better suited to "Software 1.0" (e.g. coding a calculator). But there are a huge number of domains, some highlighted in the article, which usually have to do with an infinite possible number of inputs and outputs, where human written code is not faster or more correct than a neural network.

Similarly, I don't whether AI weights are computed all in virtual parallel, but if they are computing every node simultaneously that will be less efficient than the Neumann model in which a Program Counter (PC) acts like a "cursor" hopping arund and iterating states of the discrete code model at points throughout it. E.g. a video game with a controller and various sprites will have objects that update at various rates and the player moves with different code underwater than in the air, than on land, so different parts of the model would execute.