That seems true in a world where we completely stop looking at code. Right now my reality is giant verbose PRs written by agents in Java, and it sucks to review.
So I guess the idea is tastemaker humans spend all their time coming up with sweet macros and telling agents to refactor the ever shrinking code base.
But even still I think the main argument for S-Expressions (it’s easiest for humans to create DSLs by working on serialized AST) has become weaker. If DSLs are the best way to make a code base readable, you can now have your agent create one with whatever syntax you like!
I think these things are going to be true:
- For a specific well-defined coding task, an LLM coding agent will be able to execute it quicker than humans, regardless of how it does it.
- Codebases which are well organised will be easier for an LLM to manipulate than codebases which are obscure and surprising.
I mean.. I think it's plausible that something like "lisp makes DSLs easy, DSLs are a great way to arrange code" is especially true with LLMs; but I'd also think you'd need to know when to use (& when to not use) DSLs to get effective results from this.
Whereas, say, you could have the LLM come up with a DSL in a way which is high friction for the LLM to do it ... but, you'd get the result a bit slower, & maybe worse.
I’ve been experimenting with stuff like “this project is a Kahn process network, every change is strict conformer to that model, add features only by adding nodes”. As a bonus if you’re strict then porting the specific implementation to a new language should be easy.
To me stuff like this feels like the future of programming.. details don’t matter but design language does. not leet code of fancy data structures from scratch but at least a basic familiarity with LOTS of them, a working knowledge of vast zoos of expressive patterns and automata, etc.