As noted in another comment, that is legit program synthesis from a complete specification in natural language. The workflow described in the paper and abstracted in your comment can be very useful, as long as the user can have good confidence in the correctness of the results (or the ability to eyball and correct them as needed).

The problem is that this approach relies on a large language model trained on a copy of the entire web (GPT-3, trained on Common Crawl plus network extras), fine-tuned on github (Codex) and then fine-tuned again on the kinds of problems that it is supposed to solve. That is an insane amount of resources to spend to solve simple programming problems with known solutions that can be implemented by hand at much lower cost and effort. In that sense it's a little bit disappointing: so much data, so much compute, and all you can do is tell computer how to code python?