I think this confuses two different things:

- LLMs can act as pseudocode to code translators (they are excellent at this)

- LLMs still create bugs and make errors, and a reasonable hypothesis is at a rate in direct proportion to the "complexity" or "buggedness" of the underlying language.

In other words, give an AI a footgun and it will happily use it unawares. That doesn't mean however it can't rapidly turn your pseudocode into code.

None of this means that LLMs can magically correct your pseudocode at all times if your logic is vastly wrong for your goal, but I do believe they'll benefit immensely from new languages that reduce the kind of bugs they make.

This is the moment we can create these languages. Because LLMs can optimize for things that humans can't, so it seems possible to design new languages to reduce bugs in ways that work for LLMs, but are less effective for people (due to syntax, ergonomics, verbosity, anything else).

This is crucially important. Why? Because 99% of all code written in the next two decades will be written by AI. And we will also produce 100x more code than has ever been written before (because the cost of doing it, has dropped essentially to zero). This means that, short of some revolutions in language technology, the number of bugs and vulnerabilities we can expect will also 100x.

That's why ideas like this are needed.

I believe in this too and am working on something also targeting LLMs specifically, and have been working on it since Mid to Late November last year. A business model will make such a language sustainable.