[mikestorrent]

I agree with your sentiment. The thing is, in the past, the abstractions supporting us were designed for our (human) use, and we had to understand their surface interface in order to be able to use them effectively.

Now, we're driving such things with AI; it follows that we will see better results if we do some of the work climbing down into the supporting abstractions to make their interface more suitable for AI use. To extend your cooking metaphor, it's time to figure out the manufactured food megafactory now; yes, we're still "cooking" in there, but you might not recognize the spatulas.

Things like language servers (LSPs) are a step in this direction: making it possible to interact with the language's parser/linter/etc before compile/runtime. I think we'll eventually see that some programming languages end up being more apropos to efficiently get working, logically organized code out of an AI; whether that is languages with "only one way to do things" and extremely robust and strict typing, or something more like a Lisp with infinite flexibility where you can make your own DSLs etc remains to be seen.

Frameworks will also evolve to be AI-friendly with more tooling akin to an LSP that allows an MCP-style interaction from the agent with the codebase to reason about it. And, ultimately, whatever is used the most and has the most examples for training will probably win...