> But a programmer+LLM is going to be far more effective in any language than an unaided programmer is in Lisp—and a programmer+LLM is going to be more effective in a popular language with a large training set, such as Java, TypeScript, Kotlin, or Rust, than in Lisp. So in a world with LLMs, the main practical reason to choose Lisp disappears.

You are working on the assumption that humans don't need to even look at the code ever again. At this point it in time, it is not true.

The trajectory over the last 3 years do not lead me to believe that it will be true in the future.

But, lets assume that in some future, it is true: If that is the case, then Lisp is a better representation than those other languages for LLMs to program in; after all, why have the LLMs write in Javascript (or Java, or Rust, or whatever), which a compiler backend lowers into an AST, which then gets lowered into machine code.

Much better to program in the AST itself.

IOW, why program in the intermediate language like JS, Java, Rust, etc when you can program in the lowered language?

For humans, using the JS, Java or Rust lets us verbosely describe whatever the AST is in terms humans can understand, however the more compact AST is unarguably better for the way LLMs work (token prediction).

So, in a world where all code is written by LLMs, using an intermediate verbose language is not going to happen unless the prompter specifically forcibly selects a language.

> The trajectory over the last 3 years do not lead me to believe that it will be true in the future.

Everything changed in November of 2025 with Opus 4.5 and GPT 5.2 a short time later. StrongDM is now building out complex systems with zero human intervention. Again, stop and actually use these models first, then engage in discussion about what they can and can't do.

> But, lets assume that in some future, it is true: If that is the case, then Lisp is a better representation than those other languages for LLMs to program in; after all, why have the LLMs write in Javascript (or Java, or Rust, or whatever), which a compiler backend lowers into an AST, which then gets lowered into machine code.

That's your human brain thinking it knows better. The "bitter lesson" of AI is that more data=better performance and even if you try to build a system that encapsulates human-brain common sense, it will be trounced by a system simply trained on more data.

There is vastly, vastly more training data for JavaScript, Java, and Rust than there is for Lisp. So, in the real world, LLMs perform better with those. Unlike us, they don't give a shit about notation. All forms of token streams look alike to them, whether they involve a lot of () or a lot of {;}.