So when we want deterministic process, we invent a set of labels where each is a singleton. Alongside them is a set of rules that specify how to describe their transformation. Then we invented machines that can interpret those instructions. The main advantage was that we know the possible outputs (assuming a good reliability) before we even have to act.

LLMs don't work so well in that regard, as while they have a perfect embedding of textual grammar rules, they don't have a good representation for what those labels refers to. All they have are relations between labels and how likely are they used together. But not what are the sets that those labels refer to and how the items in those sets interact.

We want software to be operationally precise because it allows us to build up towers of abstractions without needing to worry about leaks (even the leakiest software abstraction is far more watertight than any physical "abstraction").

But, at the level of the team or organization that's _building_ the software, there's no such operational precision. Individuals communicating with each other drop down to such precision when useful, but at any endeavor larger than 2-3 people, the _vast_ majority of communication occurs in purely natural language. And yet, this still generates useful software.

The phase change of LLMs is that they're computers that finally are "smart" enough to engage at this level. This is fundamentally different from the world Dijkstra was living in.