I may be over-reading, but I think this kind of example not only demonstrates the pragmatic legal issues, but also the fundamental weaknesses of a solely text-oriented approach to suggesting code. It doesn't really seem to have a representation of the problem being solved, or the relationship between things it generates and such a goal. This is not surprising in a tool which claims to work at least a little for almos…
I think it’s pretty clear that program synthesis good enough to replace programmers requires AGI. This solely text based approach is simply “easy” to do, and that’s why we see it. I think it’s cool and results are intriguing but the approach is fundamentally weak and IMO breakthroughs are needed to truly solve the problem of program synthesis.
You need either a) a complete specification of the target program in a formal language (other than the target language) or b) an incomplete specification in the form of positive and negative examples of the inputs and outputs of the target program, and maybe some form of extra inductive bias to direct the search for a correct program [edit: the latter setting is more often known as program induction].
In the last few years the biggest, splashiest result in program synthesis was the work behind FlashFill, from Gulwani et al: one-shot program learning, and that's one shot, from a single example, not with a model pretrained on millions of examples. It works with lots of hand-crafted DSLs that try to capture the most common use-cases, a kind of programming common sense that, e.g. tells the synthesiser that if the input is "Mr. John Smith" and the output is "Mr" then if the input is "Ms Jane Brown" the output should be "Ms". It works really, really well but you didn't hear about it because it's not deep learning and so it's not as overhyped.
Copilot tries to circumvent the need for "programming common sense" by combining the spectacular ability of neural nets to interpolate between their training data with billions of examples of code snippets, in order to overcome their also spectacular inability to extrapolate. Can language models learned with neural nets replace the work of hand-crafting DSLs with the work of collecting and labelling petabytes of data? We'll have to wait and see. There are also many approaches that don't rely on hand-crafted DSLs, and also work really, really well (true one-shot learning of recursive programs without an example of the base case and the synthesis terminates) but those generally only work for uncommon programming languages like Prolog or Haskell, so they're not finding their way to your IDE, or your spreadsheet app, any time soon.
But, no, AGI is not needed for program synthesis. What's really needed I think is more visibility of program synthesis research so programmers like yourself don't think it's such an insurmountable problem that it can only be solved by magickal AGI.