[avaer]

I think it won't be too different once we see a few upgrades that are going to be required for reliability (and scaling up the AI assisted engineering process):

  - deterministic agents, where the model guarantees the same output with a seed
  - much faster coding agents, which will allow us to "compile" or "execute" natural language without noticing the llm
  - maybe just running the whole thing locally so privacy and reliability are not an issue

We're not there yet, but once we have that then I agree there won't be too much of a difference between using a high level language and plain text.

There's going to be a massive shift in programming education though, because knowing an actual programming language won't matter any more than knowing assembly does today.

[just6979]

Then those "upgrades" will come down to just using an LLM as a lexer/parser for natural language and then calling a compiler on the generated AST. Except natural language is often very very ambiguous and removing that ambiguity by limiting the possible inputs just brings you closer and closer to a high level programming language. So why not just start there and use something way more efficient than an LLM for lexing/parsing? I'm not saying current high level languages are the endgame, they can certainly be improved and specialized and made faster. Just that the current architecture does not need to be replaced by statistical modeling, especially when you talk of making them deterministic with starting seeds... why bother forcing an LLM to follow the same deterministic path when we already know how to make tools to do that?